import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
import pandas as pd
import numpy as np
pd.set_option('future.no_silent_downcasting', True)
data = pd.read_csv(r'data/data.csv', sep='|', dtype={'client_id':int},skipfooter = 1, engine='python')

data.shape

(238615, 77)

[x for x in data.columns if x.startswith('Saving')]

['SavingAccount_Active_ARG_Salary',
 'SavingAccount_Active_ARG',
 'SavingAccount_Active_DOLLAR',
 'SavingAccount_Balance_FirstDate',
 'SavingAccount_Balance_LastDate',
 'SavingAccount_Balance_Average',
 'SavingAccount_Days_with_use',
 'SavingAccount_Days_with_Credits',
 'SavingAccount_Days_with_Debits',
 'SavingAccount_Salary_Payment_Transactions',
 'SavingAccount_Transfer_In_Transactions',
 'SavingAccount_ATM_Extraction_Transactions',
 'SavingAccount_Service_Payment_Transactions',
 'SavingAccount_CreditCard_Payment_Transactions',
 'SavingAccount_Transfer_Out_Transactions',
 'SavingAccount_DebitCard_Spend_Transactions',
 'SavingAccount_Transactions_Transactions',
 'SavingAccount_Credits_Transactions',
 'SavingAccount_Debits_Transactions',
 'SavingAccount_Salary_Payment_Amount',
 'SavingAccount_Transfer_In_Amount',
 'SavingAccount_ATM_Extraction_Amount',
 'SavingAccount_Service_Payment_Amount',
 'SavingAccount_CreditCard_Payment_Amount',
 'SavingAccount_Transfer_Out_Amount',
 'SavingAccount_DebitCard_Spend_Amount',
 'SavingAccount_Total_Amount',
 'SavingAccount_Credits_Amounts',
 'SavingAccount_Debits_Amounts']

data.Month.value_counts()

Month
2019-04-01    26547
2019-03-01    26547
2019-02-01    26512
2019-01-01    26503
2018-08-01    26502
2018-10-01    26501
2018-09-01    26501
2018-12-01    26501
2018-11-01    26501
Name: count, dtype: int64

len(data.client_id.unique())

26560

#clientes en la base 26560

data_9m = pd.DataFrame(data.client_id.value_counts().reset_index())
data_9m.columns = ['client_id','cantidad_meses']

data_9m

data_9m[data_9m.cantidad_meses == 9].shape

(26483, 2)

#me quedo con los de 9 meses
data_9m = data_9m[data_9m.cantidad_meses == 9].copy()

#sin paquetes en el ultimo mes

data.Package_Active.value_counts()

Package_Active
No     234177
Yes      4438
Name: count, dtype: int64

#tenemos 9 meses, pero usaremos 2 para la prediccion, 1 para lead window y el resto pata el entrenamiento
data_sin_paquete = data[(data.Package_Active == 'No') & (data.Month == '2019-01-01')][['client_id']]

data_sin_paquete.shape

(26026, 1)

#condicion comercial, sera que el cliente tenga cobranding( que es lo de coto, cencusud, etc)

len(data[(data.CreditCard_CoBranding == 'Yes') & (data.Month == '2019-01-01')].client_id.unique())

2843

#prediction Window

data.Target.value_counts()

Target
0.0    176359
1.0     62256
Name: count, dtype: int64

data_Target = data[(data.Target == 1) & (data.Month.isin(['2019-04-01','2019-03-01']))][['client_id']].drop_duplicates()
data_Target.shape

data_Target['TGT'] = 1

data_cobranding = data[(data.CreditCard_CoBranding == 'Yes') & (data.Month == '2019-01-01')][['client_id']]
data_cobranding.shape

(2843, 1)

data_cruce_cobranding = data_cobranding.merge(data_Target, how='left', on='client_id').fillna(0)

data_cruce_cobranding.TGT.value_counts()

TGT
0.0    2836
1.0       7
Name: count, dtype: int64

# como vemos, los que tienen cobranding y con target 1, son solo 7. Esos no los voy a usar, son muy pocos, ademas
# hay una constraint de negocio de los bancos, que dice que a los que tienen cobranding, no les doy paquetes
#porque no tienen  mucha plata

## asi que lo que necesitaria es para cumplir el objetivo de "Vender paquetes" es:
# Cruzar todo y quedarse con los clientes aptos:
# 9 mese de data (no le vendo a los clientes nuevos)
# sin cobranding (porque si tiene cobranding se que no les voy a vender a ellos)
# sin paquete activo (no le voy a vender un producto que ya tiene)

data_cobranding_No = data[(data.CreditCard_CoBranding == 'No') & (data.Month == '2019-01-01')][['client_id']]

data_cobranding_No

universo = data_9m.merge(data_sin_paquete, how='inner', on='client_id')\
                .merge(data_cobranding_No, how='inner', on='client_id')\
                .merge(data_Target, how='left', on='client_id').fillna(0)

universo.TGT.value_counts()

TGT
0.0    16368
1.0     6823
Name: count, dtype: int64

#en nuestro universo el 30% de las personas compran paquetes

training_window = data[(data.Month.isin(['2018-08-01','2018-09-01','2018-10-01','2018-11-01','2018-12-01','2019-01-01']))]
training_window.shape

(159009, 77)

training_window

training_window.client_id.value_counts()

client_id
5856970    6
3852147    6
1129478    6
711456     6
2531821    6
          ..
6264756    2
5124642    1
6623284    1
1419642    1
6858355    1
Name: count, Length: 26509, dtype: int64

#para quitar esos clientes que no tienen 6 registros,
#cruzare mi training_window(estos serian 6 meses) con el universo de trabajo(estos son nueve meses)

training_window = training_window.merge(universo, how='inner', on='client_id')

training_window.client_id.value_counts()

client_id
5856970    6
2428341    6
6454439    6
5678167    6
7308381    6
          ..
6210931    6
2758381    6
6348905    6
5982253    6
5967858    6
Name: count, Length: 23191, dtype: int64

#crear data frame con IF

dfIdentityFeatures = training_window[training_window.Month == '2019-01-01'][['client_id','Target','Investment_Numbers','CreditCard_Total_Limit']]
dfIdentityFeatures

dfTransformFeatures = training_window[training_window.Month == '2019-01-01'][['client_id']]

columnas = ['CreditCard_Premium','CreditCard_Active','CreditCard_CoBranding','Loan_Active',
            'Mortgage_Active', 'SavingAccount_Active_ARG_Salary','SavingAccount_Active_ARG','SavingAccount_Active_DOLLAR'
           ,'DebitCard_Active','Investment_Active','Package_Active','Insurance_Life'
           ,'Insurance_Home','Insurance_Accidents','Insurance_Mobile','Insurance_ATM','Insurance_Unemployment']

for columna in columnas:
    dfTransformFeatures[columna] = np.where(training_window[training_window.Month == '2019-01-01'][columna] == 'Yes',1,0)
#----------    

dfTransformFeatures['Sex'] = np.where(training_window[training_window.Month == '2019-01-01']['Sex'] == 'F', 0, 1)

#--------
TiposSegurosColumnas = [x for x in training_window.columns if x.startswith('Insurance')]

dfInsurance = training_window[training_window.Month == '2019-01-01'][['client_id']]
for columna in TiposSegurosColumnas:
    dfInsurance[columna] = np.where(training_window[training_window.Month == '2019-01-01'][columna] == 'Yes',1,0)

dfTransformFeatures['Total_Seguros_del_UltimosMes'] = dfInsurance[TiposSegurosColumnas[0]] + dfInsurance[TiposSegurosColumnas[1]] + dfInsurance[TiposSegurosColumnas[2]] +dfInsurance[TiposSegurosColumnas[3]] +dfInsurance[TiposSegurosColumnas[4]]+dfInsurance[TiposSegurosColumnas[5]]


#---------------
dfTransformFeatures['Client_Age_grp'] = training_window[training_window.Month == '2019-01-01'][['Client_Age_grp']]

di = { 
 "Entre 40 y 49 años"          : 40,
 "Entre 30 y 39 años"                 : 30,
    "Entre 50 y 59 años"                 : 50,
    "Entre 60 y 64 años"                 : 60,
    "Entre 65 y 69 años"                 : 65,
    "Entre 18 y 29 años"                 : 18,
    "Mayor a 70 años"                 : 70,
    "Menor a 18 años"                 : 17,
 }
dfTransformFeatures.Client_Age_grp = dfTransformFeatures.Client_Age_grp.map(di)

#-----------------

#dfTransformFeatures['MaxSavingAccount_ATM_Extraction_Last6'] = tr

# total cantidad de seguros del ultimo mes,  grupos de edades ponerlos en numeros, maxima extraccion de atm de los ultmos 6 meses
dfTransformFeatures

training_window.columns[training_window.isnull().any()].tolist()

['SavingAccount_Balance_Average', 'Region', 'CreditCard_Product']

training_window[training_window.SavingAccount_Balance_Average.fillna(-999) == -999]

training_window[training_window.client_id == '6412619']['SavingAccount_Balance_Average']

Series([], Name: SavingAccount_Balance_Average, dtype: float64)

training_window['SavingAccount_Balance_Average'] = np.where(training_window.SavingAccount_Balance_Average.isnull(),
    training_window.SavingAccount_Balance_FirstDate - training_window.SavingAccount_Debits_Amounts + training_window.SavingAccount_Credits_Amounts,
    training_window.SavingAccount_Balance_Average)

training_window[training_window.client_id == '6412619']['SavingAccount_Balance_Average']

Series([], Name: SavingAccount_Balance_Average, dtype: float64)

training_window[training_window.Region.fillna('Empty') == 'Empty']

data[data.Month == '2019-04-01']

training_window.Region.value_counts()

Series([], Name: count, dtype: int64)

training_window['Region'].describe()

count       0
unique      0
top       NaN
freq      NaN
Name: Region, dtype: object

training_win_buffer = training_window.copy()

data_last_Moth = data[data.Month == '2019-04-01'][['client_id','Region']].copy()
data_last_Moth.rename(columns={'Region':'RegionUpdated'}, inplace=True)

training_win_buffer = training_win_buffer.merge(data_last_Moth, how='left', on='client_id')

training_window['Region'] = training_win_buffer['RegionUpdated']

training_window

training_window['Region'].describe()

count           139110
unique               7
top       BUENOS AIRES
freq             41904
Name: Region, dtype: object

training_window.Region.value_counts()

Region
BUENOS AIRES                     41904
REGION CENTRO                    27474
REGION NORTE GRANDE ARGENTINO    22044
REGION PATAGONICA                14592
CABA Centro/Norte                12288
AMBA Resto                       10968
REGION CUYO                       9840
Name: count, dtype: int64

training_window[training_window.Region.fillna('Empty') == 'Empty']

data[(data.Month == '2019-04-01') & (data.client_id == '833913')]

data[data.client_id == '833913']

training_window['Region'] = np.where(training_window['Region'].isna(), 
                                                          'BUENOS AIRES',
                                                         training_window['Region'])

training_window[training_window.Region.fillna('Empty') == 'Empty']

training_window.Region.value_counts()

Region
BUENOS AIRES                     41940
REGION CENTRO                    27474
REGION NORTE GRANDE ARGENTINO    22044
REGION PATAGONICA                14592
CABA Centro/Norte                12288
AMBA Resto                       10968
REGION CUYO                       9840
Name: count, dtype: int64

training_window.CreditCard_Product.value_counts()

Series([], Name: count, dtype: int64)

training_window[training_window.CreditCard_Product.fillna('Empty') == 'Empty']

training_window['CreditCard_Product'].describe()

count       0
unique      0
top       NaN
freq      NaN
Name: CreditCard_Product, dtype: object

CreditActive_clients = training_window[(training_window.CreditCard_Active == 'Yes') & (training_window.Month == '2019-01-01')]['client_id']

CreditCardProduct_buffer = data[(data.Month == '2019-04-01') & (data.client_id.isin(CreditActive_clients))][['client_id','CreditCard_Product']].copy()

CreditCardProduct_buffer.rename(columns={'CreditCard_Product':'CreditCard_ProductUpdated'}, inplace=True)

training_win_buffer2 = training_window.copy()

training_win_buffer2 = training_win_buffer2.merge(CreditCardProduct_buffer, how='left', on='client_id')

training_window['CreditCard_Product'] = training_win_buffer2['CreditCard_ProductUpdated']

training_window

training_window['CreditCard_Product'].describe()

count              88590
unique                 7
top       J55660104XX012
freq               49572
Name: CreditCard_Product, dtype: object

training_window.CreditCard_Product.value_counts()

CreditCard_Product
J55660104XX012    49572
J55660202XX012    34554
J55660102XX012     2412
J55660702XX012     1494
J55661002XX012      372
J55660124XX012      180
J55660123XX012        6
Name: count, dtype: int64

training_window[training_window.CreditCard_Product.fillna('Empty') == 'Empty']

data[data.client_id == '5967858']

training_window['CreditCard_Product'] = np.where(training_window['CreditCard_Product'].isna(), 
                                                          'No',
                                                         training_window['CreditCard_Product'])

training_window[training_window.CreditCard_Product.fillna('Empty') == 'Empty']

training_window.CreditCard_Product.value_counts()

CreditCard_Product
No                50556
J55660104XX012    49572
J55660202XX012    34554
J55660102XX012     2412
J55660702XX012     1494
J55661002XX012      372
J55660124XX012      180
J55660123XX012        6
Name: count, dtype: int64

training_window.columns[training_window.isnull().any()].tolist()

[]

training_window['SavingAccount_Days_with_use'].describe()

count    139146.000000
mean          2.512685
std           3.831400
min           0.000000
25%           0.000000
50%           0.000000
75%           4.000000
max          41.000000
Name: SavingAccount_Days_with_use, dtype: float64

import seaborn as sns
import matplotlib.pyplot as plt

# plot a distribution plot
sns.displot(training_window['SavingAccount_Balance_Average'], kind='kde')

# display the plot
plt.show()

plt.boxplot(training_window['SavingAccount_Balance_Average'])

# add labels for five number summary
plt.text(x = 1.1, y = training_window['SavingAccount_Balance_Average'].min(), s ='min')
plt.text(x = 1.1, y = training_window['SavingAccount_Balance_Average'].quantile(0.25), s ='Q1')
plt.text(x = 1.1, y = training_window['SavingAccount_Balance_Average'].median(), s ='meadian (Q2)')
plt.text(x = 1.1, y = training_window['SavingAccount_Balance_Average'].quantile(0.75), s ='Q3')
plt.text(x = 1.1, y = training_window['SavingAccount_Balance_Average'].max(), s ='max')


# add the graph title and axes labels
plt.title('Boxplot of Total Bill Amount')
plt.ylabel('Total bill')

# display the plot
plt.show()

training_window['SavingAccount_Balance_Average'].describe()

count    1.391460e+05
mean     4.094871e+03
std      2.293939e+04
min     -5.665900e+02
25%      0.000000e+00
50%      1.032500e+01
75%      1.473138e+03
max      1.771201e+06
Name: SavingAccount_Balance_Average, dtype: float64

import seaborn as sns
import matplotlib.pyplot as plt

# plot a distribution plot
sns.displot(training_window['SavingAccount_Balance_Average'], kind='kde')

# display the plot
plt.show()

p95 = training_window['SavingAccount_Balance_Average'].quantile(0.95)
p99 = training_window['SavingAccount_Balance_Average'].quantile(0.99)
three_sigma = 3 * training_window['SavingAccount_Balance_Average'].std()

print('p95         ', p95)
print('p99         ', p99)
print('Three sigma ', three_sigma)

p95          17043.66
p99          70055.67999999813
Three sigma  68818.178586892

training_window['SavingAccount_Balance_Average'] = np.where(training_window['SavingAccount_Balance_Average'] > three_sigma, 
                                                          three_sigma,
                                                         training_window['SavingAccount_Balance_Average'])

training_window['SavingAccount_Balance_Average'].describe()

count    139146.000000
mean       3179.457488
std        9821.897057
min        -566.590000
25%           0.000000
50%          10.325000
75%        1473.137500
max       68818.178587
Name: SavingAccount_Balance_Average, dtype: float64

import seaborn as sns
import matplotlib.pyplot as plt

# plot a distribution plot
sns.displot(training_window['SavingAccount_Balance_Average'], kind='kde')

# display the plot
plt.show()

training_window['SavingAccount_Balance_Average'] = np.where(training_window['SavingAccount_Balance_Average'] > p95, 
                                                          p95,
                                                         training_window['SavingAccount_Balance_Average'])

training_window['SavingAccount_Balance_Average'].describe()

count    139146.000000
mean       2026.112910
std        4351.634834
min        -566.590000
25%           0.000000
50%          10.325000
75%        1473.137500
max       17043.660000
Name: SavingAccount_Balance_Average, dtype: float64

import seaborn as sns
import matplotlib.pyplot as plt

# plot a distribution plot
sns.displot(training_window['SavingAccount_Balance_Average'], kind='kde')

# display the plot
plt.show()

variables_analisis_outliers = ['SavingAccount_Days_with_Credits',
                               'SavingAccount_Days_with_Debits',
                               'SavingAccount_Salary_Payment_Transactions',
                               'SavingAccount_Transfer_In_Transactions',
                               'SavingAccount_ATM_Extraction_Transactions',
                               'SavingAccount_CreditCard_Payment_Transactions',
                               'SavingAccount_Transfer_Out_Transactions',
                               'SavingAccount_DebitCard_Spend_Transactions',
                               'SavingAccount_Transactions_Transactions',
                               'SavingAccount_Credits_Transactions',
                               'SavingAccount_Debits_Transactions',
                               'SavingAccount_Salary_Payment_Amount',
                               'SavingAccount_Transfer_In_Amount',
                               'SavingAccount_ATM_Extraction_Amount',
                               'SavingAccount_CreditCard_Payment_Amount',
                               'SavingAccount_Transfer_Out_Amount',
                               'SavingAccount_DebitCard_Spend_Amount',
                               'SavingAccount_Total_Amount',
                               'SavingAccount_Credits_Amounts',
                               'SavingAccount_Debits_Amounts',
                               'Operations_HomeBanking',
                               'Operations_Mobile',
                               'CreditCard_Balance_ARG',
                               'CreditCard_Balance_DOLLAR',
                               'CreditCard_Total_Limit',
                               'CreditCard_Total_Spending',
                               'CreditCard_Spending_1_Installment',
                               'CreditCard_Spending_CrossBoarder',
                               'CreditCard_Spending_Aut_Debits',
                               'CreditCard_Revolving']

for variables_analizables in variables_analisis_outliers:
    print('************************************************************ ')
    print('Variable ', variables_analizables)
    print(' ')
    
    training_window[variables_analizables].describe()
    # plot a distribution plot
    sns.displot(training_window[variables_analizables], kind='kde')

    # display the plot
    plt.show()
    
    plt.boxplot(training_window[variables_analizables])

    # add labels for five number summary
    plt.text(x = 1.1, y = training_window[variables_analizables].min(), s ='min')
    plt.text(x = 1.1, y = training_window[variables_analizables].quantile(0.25), s ='Q1')
    plt.text(x = 1.1, y = training_window[variables_analizables].median(), s ='meadian (Q2)')
    plt.text(x = 1.1, y = training_window[variables_analizables].quantile(0.75), s ='Q3')
    plt.text(x = 1.1, y = training_window[variables_analizables].max(), s ='max')


    # add the graph title and axes labels
    plt.title('Boxplot of Total Bill Amount')
    plt.ylabel('Total bill')

    # display the plot
    plt.show()
    
    p95 = training_window[variables_analizables].quantile(0.95)
    p99 = training_window[variables_analizables].quantile(0.99)
    three_sigma = 3 * training_window[variables_analizables].std()

    print('p95         ', p95)
    print('p99         ', p99)
    print('Three sigma ', three_sigma)
    
    training_window[variables_analizables].describe()

************************************************************ 
Variable  SavingAccount_Days_with_Credits

p95          5.0
p99          8.0
Three sigma  5.3821836766669335
************************************************************ 
Variable  SavingAccount_Days_with_Debits

p95          9.0
p99          15.0
Three sigma  9.723007390387242
************************************************************ 
Variable  SavingAccount_Salary_Payment_Transactions

p95          1.0
p99          3.0
Three sigma  1.7306415659542151
************************************************************ 
Variable  SavingAccount_Transfer_In_Transactions

p95          2.0
p99          4.0
Three sigma  3.4065241487924736
************************************************************ 
Variable  SavingAccount_ATM_Extraction_Transactions

p95          4.0
p99          12.0
Three sigma  6.793744483785929
************************************************************ 
Variable  SavingAccount_CreditCard_Payment_Transactions

p95          2.0
p99          4.0
Three sigma  2.820305708750473
************************************************************ 
Variable  SavingAccount_Transfer_Out_Transactions

p95          0.0
p99          0.0
Three sigma  0.3047514185040877
************************************************************ 
Variable  SavingAccount_DebitCard_Spend_Transactions

p95          7.0
p99          22.0
Three sigma  12.865522801673926
************************************************************ 
Variable  SavingAccount_Transactions_Transactions

p95          24.0
p99          47.0
Three sigma  28.791754836314986
************************************************************ 
Variable  SavingAccount_Credits_Transactions

p95 = training_window['SavingAccount_Days_with_Credits'].quantile(0.95)
p99 = training_window['SavingAccount_Days_with_Credits'].quantile(0.99)
three_sigma = 3 * training_window['SavingAccount_Days_with_Credits'].std()

print('p95         ', p95)
print('p99         ', p99)
print('Three sigma ', three_sigma)

p95          5.0
p99          8.0
Three sigma  5.3821836766669335

training_window['SavingAccount_Days_with_Credits'] = np.where(training_window['SavingAccount_Days_with_Credits'] > p99, 
                                                          p99,
                                                         training_window['SavingAccount_Days_with_Credits'])

training_window['SavingAccount_Days_with_Credits'].describe()

count    139146.000000
mean          1.232504
std           1.691454
min           0.000000
25%           0.000000
50%           0.000000
75%           2.000000
max           8.000000
Name: SavingAccount_Days_with_Credits, dtype: float64

# plot a distribution plot
sns.displot(training_window['SavingAccount_Days_with_Credits'], kind='kde')

# display the plot
plt.show()

p95 = training_window['SavingAccount_Days_with_Debits'].quantile(0.95)
p99 = training_window['SavingAccount_Days_with_Debits'].quantile(0.99)
three_sigma = 3 * training_window['SavingAccount_Days_with_Debits'].std()

print('p95         ', p95)
print('p99         ', p99)
print('Three sigma ', three_sigma)

p95          9.0
p99          15.0
Three sigma  9.723007390387242

training_window['SavingAccount_Days_with_Debits'] = np.where(training_window['SavingAccount_Days_with_Debits'] > three_sigma, 
                                                          three_sigma,
                                                         training_window['SavingAccount_Days_with_Debits'])

training_window['SavingAccount_Days_with_Debits'].describe()

count    139146.000000
mean          1.690302
std           2.649375
min           0.000000
25%           0.000000
50%           0.000000
75%           2.000000
max           9.723007
Name: SavingAccount_Days_with_Debits, dtype: float64

# plot a distribution plot
sns.displot(training_window['SavingAccount_Days_with_Debits'], kind='kde')

# display the plot
plt.show()

p95 = training_window['SavingAccount_Salary_Payment_Transactions'].quantile(0.95)
p99 = training_window['SavingAccount_Salary_Payment_Transactions'].quantile(0.99)
three_sigma = 3 * training_window['SavingAccount_Salary_Payment_Transactions'].std()

print('p95         ', p95)
print('p99         ', p99)
print('Three sigma ', three_sigma)

p95          1.0
p99          3.0
Three sigma  1.7306415659542151

training_window['SavingAccount_Salary_Payment_Transactions'] = np.where(training_window['SavingAccount_Salary_Payment_Transactions'] > p99, 
                                                          p99,
                                                         training_window['SavingAccount_Salary_Payment_Transactions'])

training_window['SavingAccount_Salary_Payment_Transactions'].describe()

count    139146.000000
mean          0.142534
std           0.520272
min           0.000000
25%           0.000000
50%           0.000000
75%           0.000000
max           3.000000
Name: SavingAccount_Salary_Payment_Transactions, dtype: float64

# plot a distribution plot
sns.displot(training_window['SavingAccount_Days_with_Debits'], kind='kde')

# display the plot
plt.show()

p95 = training_window['SavingAccount_Transfer_In_Transactions'].quantile(0.95)
p99 = training_window['SavingAccount_Transfer_In_Transactions'].quantile(0.99)
three_sigma = 3 * training_window['SavingAccount_Transfer_In_Transactions'].std()

print('p95         ', p95)
print('p99         ', p99)
print('Three sigma ', three_sigma)

p95          2.0
p99          4.0
Three sigma  3.4065241487924736

training_window['SavingAccount_Transfer_In_Transactions'] = np.where(training_window['SavingAccount_Transfer_In_Transactions'] > p99, 
                                                          p99,
                                                         training_window['SavingAccount_Transfer_In_Transactions'])

training_window['SavingAccount_Transfer_In_Transactions'].describe()

count    139146.000000
mean          0.314993
std           0.750827
min           0.000000
25%           0.000000
50%           0.000000
75%           0.000000
max           4.000000
Name: SavingAccount_Transfer_In_Transactions, dtype: float64

# plot a distribution plot
sns.displot(training_window['SavingAccount_Transfer_In_Transactions'], kind='kde')

# display the plot
plt.show()

p95 = training_window['SavingAccount_ATM_Extraction_Transactions'].quantile(0.95)
p99 = training_window['SavingAccount_ATM_Extraction_Transactions'].quantile(0.99)
three_sigma = 3 * training_window['SavingAccount_ATM_Extraction_Transactions'].std()

print('p95         ', p95)
print('p99         ', p99)
print('Three sigma ', three_sigma)

p95          4.0
p99          12.0
Three sigma  6.793744483785929

training_window['SavingAccount_ATM_Extraction_Transactions'] = np.where(training_window['SavingAccount_ATM_Extraction_Transactions'] > p95, 
                                                          p95,
                                                         training_window['SavingAccount_ATM_Extraction_Transactions'])

training_window['SavingAccount_ATM_Extraction_Transactions'].describe()

count    139146.000000
mean          0.320505
std           1.006634
min           0.000000
25%           0.000000
50%           0.000000
75%           0.000000
max           4.000000
Name: SavingAccount_ATM_Extraction_Transactions, dtype: float64

# plot a distribution plot
sns.displot(training_window['SavingAccount_ATM_Extraction_Transactions'], kind='kde')

# display the plot
plt.show()

p95 = training_window['SavingAccount_CreditCard_Payment_Transactions'].quantile(0.95)
p99 = training_window['SavingAccount_CreditCard_Payment_Transactions'].quantile(0.99)
three_sigma = 3 * training_window['SavingAccount_CreditCard_Payment_Transactions'].std()

print('p95         ', p95)
print('p99         ', p99)
print('Three sigma ', three_sigma)

p95          2.0
p99          4.0
Three sigma  2.820305708750473

training_window['SavingAccount_CreditCard_Payment_Transactions'] = np.where(training_window['SavingAccount_CreditCard_Payment_Transactions'] > p99, 
                                                          p99,
                                                         training_window['SavingAccount_CreditCard_Payment_Transactions'])

training_window['SavingAccount_CreditCard_Payment_Transactions'].describe()

count    139146.000000
mean          0.359170
std           0.767203
min           0.000000
25%           0.000000
50%           0.000000
75%           0.000000
max           4.000000
Name: SavingAccount_CreditCard_Payment_Transactions, dtype: float64

# plot a distribution plot
sns.displot(training_window['SavingAccount_ATM_Extraction_Transactions'], kind='kde')

# display the plot
plt.show()

p95 = training_window['SavingAccount_Transfer_Out_Transactions'].quantile(0.95)
p99 = training_window['SavingAccount_Transfer_Out_Transactions'].quantile(0.99)
three_sigma = 3 * training_window['SavingAccount_Transfer_Out_Transactions'].std()

print('p95         ', p95)
print('p99         ', p99)
print('Three sigma ', three_sigma)

p95          0.0
p99          0.0
Three sigma  0.3047514185040877

training_window.SavingAccount_Transfer_Out_Transactions.value_counts()

SavingAccount_Transfer_Out_Transactions
0.0     139099
1.0         37
2.0          3
10.0         1
9.0          1
15.0         1
25.0         1
14.0         1
4.0          1
12.0         1
Name: count, dtype: int64

# revisar, preguntar sobre esta variable: SavingAccount_Transfer_Out_Transactions

p95 = training_window['SavingAccount_DebitCard_Spend_Transactions'].quantile(0.95)
p99 = training_window['SavingAccount_DebitCard_Spend_Transactions'].quantile(0.99)
three_sigma = 3 * training_window['SavingAccount_DebitCard_Spend_Transactions'].std()

print('p95         ', p95)
print('p99         ', p99)
print('Three sigma ', three_sigma)

p95          7.0
p99          22.0
Three sigma  12.865522801673926

training_window['SavingAccount_DebitCard_Spend_Transactions'] = np.where(training_window['SavingAccount_DebitCard_Spend_Transactions'] > p99, 
                                                          p99,
                                                         training_window['SavingAccount_DebitCard_Spend_Transactions'])

training_window['SavingAccount_DebitCard_Spend_Transactions'].describe()

count    139146.000000
mean          0.991390
std           3.528173
min           0.000000
25%           0.000000
50%           0.000000
75%           0.000000
max          22.000000
Name: SavingAccount_DebitCard_Spend_Transactions, dtype: float64

# plot a distribution plot
sns.displot(training_window['SavingAccount_DebitCard_Spend_Transactions'], kind='kde')

# display the plot
plt.show()

p95 = training_window['SavingAccount_Transactions_Transactions'].quantile(0.95)
p99 = training_window['SavingAccount_Transactions_Transactions'].quantile(0.99)
three_sigma = 3 * training_window['SavingAccount_Transactions_Transactions'].std()

print('p95         ', p95)
print('p99         ', p99)
print('Three sigma ', three_sigma)

p95          24.0
p99          47.0
Three sigma  28.791754836314986

training_window['SavingAccount_Transactions_Transactions'] = np.where(training_window['SavingAccount_Transactions_Transactions'] > p99, 
                                                          p99,
                                                         training_window['SavingAccount_Transactions_Transactions'])

training_window['SavingAccount_Transactions_Transactions'].describe()

count    139146.000000
mean          4.765656
std           8.710314
min           0.000000
25%           0.000000
50%           0.000000
75%           5.000000
max          47.000000
Name: SavingAccount_Transactions_Transactions, dtype: float64

# plot a distribution plot
sns.displot(training_window['SavingAccount_Transactions_Transactions'], kind='kde')

# display the plot
plt.show()

p95 = training_window['SavingAccount_Credits_Transactions'].quantile(0.95)
p99 = training_window['SavingAccount_Credits_Transactions'].quantile(0.99)
three_sigma = 3 * training_window['SavingAccount_Credits_Transactions'].std()

print('p95         ', p95)
print('p99         ', p99)
print('Three sigma ', three_sigma)

p95          6.0
p99          11.0
Three sigma  7.817126304056519

training_window['SavingAccount_Credits_Transactions'] = np.where(training_window['SavingAccount_Credits_Transactions'] > p99, 
                                                          p99,
                                                         training_window['SavingAccount_Credits_Transactions'])

training_window['SavingAccount_Credits_Transactions'].describe()

count    139146.000000
mean          1.509573
std           2.220993
min           0.000000
25%           0.000000
50%           0.000000
75%           2.000000
max          11.000000
Name: SavingAccount_Credits_Transactions, dtype: float64

# plot a distribution plot
sns.displot(training_window['SavingAccount_Credits_Transactions'], kind='kde')

# display the plot
plt.show()

p95 = training_window['SavingAccount_Debits_Transactions'].quantile(0.95)
p99 = training_window['SavingAccount_Debits_Transactions'].quantile(0.99)
three_sigma = 3 * training_window['SavingAccount_Debits_Transactions'].std()

print('p95         ', p95)
print('p99         ', p99)
print('Three sigma ', three_sigma)

p95          19.0
p99          38.0
Three sigma  22.894184199079866

training_window['SavingAccount_Debits_Transactions'] = np.where(training_window['SavingAccount_Debits_Transactions'] > p99, 
                                                          p99,
                                                         training_window['SavingAccount_Debits_Transactions'])

training_window['SavingAccount_Debits_Transactions'].describe()

count    139146.000000
mean          3.222780
std           6.824929
min           0.000000
25%           0.000000
50%           0.000000
75%           3.000000
max          38.000000
Name: SavingAccount_Debits_Transactions, dtype: float64

# plot a distribution plot
sns.displot(training_window['SavingAccount_Debits_Transactions'], kind='kde')

# display the plot
plt.show()

p95 = training_window['SavingAccount_Salary_Payment_Amount'].quantile(0.95)
p99 = training_window['SavingAccount_Salary_Payment_Amount'].quantile(0.99)
three_sigma = 3 * training_window['SavingAccount_Salary_Payment_Amount'].std()

print('p95         ', p95)
print('p99         ', p99)
print('Three sigma ', three_sigma)

p95          22890.7775
p99          58568.9505
Three sigma  50991.24006782155

training_window['SavingAccount_Salary_Payment_Amount'] = np.where(training_window['SavingAccount_Salary_Payment_Amount'] > p95, 
                                                          p95,
                                                         training_window['SavingAccount_Salary_Payment_Amount'])

training_window['SavingAccount_Salary_Payment_Amount'].describe()

count    139146.000000
mean       1621.769465
std        5617.856081
min           0.000000
25%           0.000000
50%           0.000000
75%           0.000000
max       22890.777500
Name: SavingAccount_Salary_Payment_Amount, dtype: float64

# plot a distribution plot
sns.displot(training_window['SavingAccount_Salary_Payment_Amount'], kind='kde')

# display the plot
plt.show()

p95 = training_window['SavingAccount_Transfer_In_Amount'].quantile(0.95)
p99 = training_window['SavingAccount_Transfer_In_Amount'].quantile(0.99)
three_sigma = 3 * training_window['SavingAccount_Transfer_In_Amount'].std()

print('p95         ', p95)
print('p99         ', p99)
print('Three sigma ', three_sigma)

p95          13000.0
p99          43000.0
Three sigma  46628.46407356864

training_window['SavingAccount_Transfer_In_Amount'] = np.where(training_window['SavingAccount_Transfer_In_Amount'] > p95, 
                                                          p95,
                                                         training_window['SavingAccount_Transfer_In_Amount'])

training_window['SavingAccount_Transfer_In_Amount'].describe()

count    139146.000000
mean       1420.367686
std        3444.238763
min           0.000000
25%           0.000000
50%           0.000000
75%           0.000000
max       13000.000000
Name: SavingAccount_Transfer_In_Amount, dtype: float64

# plot a distribution plot
sns.displot(training_window['SavingAccount_Transfer_In_Amount'], kind='kde')

# display the plot
plt.show()

p95 = training_window['SavingAccount_ATM_Extraction_Amount'].quantile(0.95)
p99 = training_window['SavingAccount_ATM_Extraction_Amount'].quantile(0.99)
three_sigma = 3 * training_window['SavingAccount_ATM_Extraction_Amount'].std()

print('p95         ', p95)
print('p99         ', p99)
print('Three sigma ', three_sigma)

p95          8600.0
p99          25400.0
Three sigma  15161.853538210948

training_window['SavingAccount_ATM_Extraction_Amount'] = np.where(training_window['SavingAccount_ATM_Extraction_Amount'] > p95, 
                                                          p95,
                                                         training_window['SavingAccount_ATM_Extraction_Amount'])

training_window['SavingAccount_ATM_Extraction_Amount'].describe()

count    139146.000000
mean        642.253338
std        2102.709382
min           0.000000
25%           0.000000
50%           0.000000
75%           0.000000
max        8600.000000
Name: SavingAccount_ATM_Extraction_Amount, dtype: float64

# plot a distribution plot
sns.displot(training_window['SavingAccount_ATM_Extraction_Amount'], kind='kde')

# display the plot
plt.show()

p95 = training_window['SavingAccount_CreditCard_Payment_Amount'].quantile(0.95)
p99 = training_window['SavingAccount_CreditCard_Payment_Amount'].quantile(0.99)
three_sigma = 3 * training_window['SavingAccount_CreditCard_Payment_Amount'].std()

print('p95         ', p95)
print('p99         ', p99)
print('Three sigma ', three_sigma)

p95          11868.3125
p99          33823.049999999756
Three sigma  31971.212947940097

training_window['SavingAccount_CreditCard_Payment_Amount'] = np.where(training_window['SavingAccount_CreditCard_Payment_Amount'] > three_sigma, 
                                                          three_sigma,
                                                         training_window['SavingAccount_CreditCard_Payment_Amount'])

training_window['SavingAccount_CreditCard_Payment_Amount'].describe()

count    139146.000000
mean       1840.757643
std        5226.661125
min           0.000000
25%           0.000000
50%           0.000000
75%           0.000000
max       31971.212948
Name: SavingAccount_CreditCard_Payment_Amount, dtype: float64

# plot a distribution plot
sns.displot(training_window['SavingAccount_CreditCard_Payment_Amount'], kind='kde')

# display the plot
plt.show()

p95 = training_window['SavingAccount_Transfer_Out_Amount'].quantile(0.95)
p99 = training_window['SavingAccount_Transfer_Out_Amount'].quantile(0.99)
three_sigma = 3 * training_window['SavingAccount_Transfer_Out_Amount'].std()

print('p95         ', p95)
print('p99         ', p99)
print('Three sigma ', three_sigma)

p95          0.0
p99          21453.013999999937
Three sigma  47523.41379065669

training_window['SavingAccount_Transfer_Out_Amount'] = np.where(training_window['SavingAccount_Transfer_Out_Amount'] > p99, 
                                                          p99,
                                                         training_window['SavingAccount_Transfer_Out_Amount'])

training_window['SavingAccount_Transfer_Out_Amount'].describe()

count    139146.000000
mean        451.299070
std        2652.509583
min           0.000000
25%           0.000000
50%           0.000000
75%           0.000000
max       21453.014000
Name: SavingAccount_Transfer_Out_Amount, dtype: float64

# plot a distribution plot
sns.displot(training_window['SavingAccount_Transfer_Out_Amount'], kind='kde')

# display the plot
plt.show()

p95 = training_window['SavingAccount_DebitCard_Spend_Amount'].quantile(0.95)
p99 = training_window['SavingAccount_DebitCard_Spend_Amount'].quantile(0.99)
three_sigma = 3 * training_window['SavingAccount_DebitCard_Spend_Amount'].std()

print('p95         ', p95)
print('p99         ', p99)
print('Three sigma ', three_sigma)

p95          6350.3125
p99          20862.84349999993
Three sigma  13168.561377041347

training_window['SavingAccount_DebitCard_Spend_Amount'] = np.where(training_window['SavingAccount_DebitCard_Spend_Amount'] > p95, 
                                                          p95,
                                                         training_window['SavingAccount_DebitCard_Spend_Amount'])

training_window['SavingAccount_DebitCard_Spend_Amount'].describe()

count    139146.000000
mean        504.030718
std        1567.015956
min           0.000000
25%           0.000000
50%           0.000000
75%           0.000000
max        6350.312500
Name: SavingAccount_DebitCard_Spend_Amount, dtype: float64

# plot a distribution plot
sns.displot(training_window['SavingAccount_DebitCard_Spend_Amount'], kind='kde')

# display the plot
plt.show()

p95 = training_window['SavingAccount_Total_Amount'].quantile(0.95)
p99 = training_window['SavingAccount_Total_Amount'].quantile(0.99)
three_sigma = 3 * training_window['SavingAccount_Total_Amount'].std()

print('p95         ', p95)
print('p99         ', p99)
print('Three sigma ', three_sigma)

p95          109939.47
p99          527795.7179999923
Three sigma  536040.4438446803

training_window['SavingAccount_Total_Amount'] = np.where(training_window['SavingAccount_Total_Amount'] > p95, 
                                                          p95,
                                                         training_window['SavingAccount_Total_Amount'])

training_window['SavingAccount_Total_Amount'].describe()

count    139146.000000
mean      15375.232616
std       29201.571404
min           0.000000
25%           0.000000
50%           0.000000
75%       14466.357500
max      109939.470000
Name: SavingAccount_Total_Amount, dtype: float64

# plot a distribution plot
sns.displot(training_window['SavingAccount_Total_Amount'], kind='kde')

# display the plot
plt.show()

p95 = training_window['SavingAccount_Credits_Amounts'].quantile(0.95)
p99 = training_window['SavingAccount_Credits_Amounts'].quantile(0.99)
three_sigma = 3 * training_window['SavingAccount_Credits_Amounts'].std()

print('p95         ', p95)
print('p99         ', p99)
print('Three sigma ', three_sigma)

p95          54835.455
p99          269663.80649999913
Three sigma  271204.21506443416

training_window['SavingAccount_Credits_Amounts'] = np.where(training_window['SavingAccount_Credits_Amounts'] > p95, 
                                                          p95,
                                                         training_window['SavingAccount_Credits_Amounts'])

training_window['SavingAccount_Credits_Amounts'].describe()

count    139146.000000
mean       7558.049240
std       14577.499232
min           0.000000
25%           0.000000
50%           0.000000
75%        7000.250000
max       54835.455000
Name: SavingAccount_Credits_Amounts, dtype: float64

# plot a distribution plot
sns.displot(training_window['SavingAccount_Credits_Amounts'], kind='kde')

# display the plot
plt.show()

## quitando outliers Variable: SavingAccount_Debits_Amounts

p95 = training_window['SavingAccount_Debits_Amounts'].quantile(0.95)
p99 = training_window['SavingAccount_Debits_Amounts'].quantile(0.99)
three_sigma = 3 * training_window['SavingAccount_Debits_Amounts'].std()

print('p95         ', p95)
print('p99         ', p99)
print('Three sigma ', three_sigma)

p95          54108.784999999996
p99          268277.130499996
Three sigma  269760.6696250981

training_window['SavingAccount_Debits_Amounts'] = np.where(training_window['SavingAccount_Debits_Amounts'] > p95, 
                                                          p95,
                                                         training_window['SavingAccount_Debits_Amounts'])

training_window['SavingAccount_Debits_Amounts'].describe()

count    139146.000000
mean       7539.658027
std       14341.236934
min           0.000000
25%           0.000000
50%           0.000000
75%        7102.057500
max       54108.785000
Name: SavingAccount_Debits_Amounts, dtype: float64

# plot a distribution plot
sns.displot(training_window['SavingAccount_Debits_Amounts'], kind='kde')

# display the plot
plt.show()

p95 = training_window['Operations_HomeBanking'].quantile(0.95)
p99 = training_window['Operations_HomeBanking'].quantile(0.99)
three_sigma = 3 * training_window['Operations_HomeBanking'].std()

print('p95         ', p95)
print('p99         ', p99)
print('Three sigma ', three_sigma)

p95          7.0
p99          15.0
Three sigma  8.928026498997632

training_window['Operations_HomeBanking'] = np.where(training_window['Operations_HomeBanking'] > p99, 
                                                          p99,
                                                         training_window['Operations_HomeBanking'])

training_window['Operations_HomeBanking'].describe()

count    139146.000000
mean          1.213186
std           2.761946
min           0.000000
25%           0.000000
50%           0.000000
75%           1.000000
max          15.000000
Name: Operations_HomeBanking, dtype: float64

# plot a distribution plot
sns.displot(training_window['Operations_HomeBanking'], kind='kde')

# display the plot
plt.show()

p95 = training_window['Operations_Mobile'].quantile(0.95)
p99 = training_window['Operations_Mobile'].quantile(0.99)
three_sigma = 3 * training_window['Operations_Mobile'].std()

print('p95         ', p95)
print('p99         ', p99)
print('Three sigma ', three_sigma)

p95          5.0
p99          14.0
Three sigma  7.72057758014084

training_window['Operations_Mobile'] = np.where(training_window['Operations_Mobile'] > p99, 
                                                          p99,
                                                         training_window['Operations_Mobile'])

training_window['Operations_Mobile'].describe()

count    139146.000000
mean          0.624617
std           2.277957
min           0.000000
25%           0.000000
50%           0.000000
75%           0.000000
max          14.000000
Name: Operations_Mobile, dtype: float64

# plot a distribution plot
sns.displot(training_window['Operations_Mobile'], kind='kde')

# display the plot
plt.show()

p95 = training_window['CreditCard_Balance_ARG'].quantile(0.95)
p99 = training_window['CreditCard_Balance_ARG'].quantile(0.99)
np99 = -training_window['CreditCard_Balance_ARG'].quantile(0.99)
three_sigma = 3 * training_window['CreditCard_Balance_ARG'].std()

print('p95         ', p95)
print('p99         ', p99)
print('np99         ', np99)
print('Three sigma ', three_sigma)

p95          30815.11
p99          55138.89599999998
np99          -55138.89599999998
Three sigma  35962.00973574623

training_window['CreditCard_Balance_ARG'] = np.where(training_window['CreditCard_Balance_ARG'] > p99, 
                                                          p99,
                                                         training_window['CreditCard_Balance_ARG'])

training_window['CreditCard_Balance_ARG'] = np.where(training_window['CreditCard_Balance_ARG'] < np99, 
                                                          np99,
                                                         training_window['CreditCard_Balance_ARG'])

training_window['CreditCard_Balance_ARG'].describe()

count    139146.000000
mean       7084.210404
std       10832.826535
min      -55138.896000
25%           0.000000
50%        2716.210000
75%        9483.642500
max       55138.896000
Name: CreditCard_Balance_ARG, dtype: float64

# plot a distribution plot
sns.displot(training_window['CreditCard_Balance_ARG'], kind='kde')

# display the plot
plt.show()

p95 = training_window['CreditCard_Balance_DOLLAR'].quantile(0.95)
p99 = training_window['CreditCard_Balance_DOLLAR'].quantile(0.99)
np99 = -training_window['CreditCard_Balance_DOLLAR'].quantile(0.99)
three_sigma = 3 * training_window['CreditCard_Balance_DOLLAR'].std()

print('p95         ', p95)
print('p99         ', p99)
print('np99         ', np99)
print('Three sigma ', three_sigma)

p95          0.99
p99          132.93649999999963
np99          -132.93649999999963
Three sigma  200.20502558797813

training_window['CreditCard_Balance_DOLLAR'] = np.where(training_window['CreditCard_Balance_DOLLAR'] > p99, 
                                                          p99,
                                                         training_window['CreditCard_Balance_DOLLAR'])

training_window['CreditCard_Balance_DOLLAR'] = np.where(training_window['CreditCard_Balance_DOLLAR'] < np99, 
                                                          np99,
                                                         training_window['CreditCard_Balance_DOLLAR'])

training_window['CreditCard_Balance_DOLLAR'].describe()

count    139146.000000
mean          2.161760
std          15.995588
min        -132.936500
25%           0.000000
50%           0.000000
75%           0.000000
max         132.936500
Name: CreditCard_Balance_DOLLAR, dtype: float64

# plot a distribution plot
sns.displot(training_window['CreditCard_Balance_DOLLAR'], kind='kde')

# display the plot
plt.show()

p95 = training_window['CreditCard_Total_Limit'].quantile(0.95)
p99 = training_window['CreditCard_Total_Limit'].quantile(0.99)
three_sigma = 3 * training_window['CreditCard_Total_Limit'].std()

print('p95         ', p95)
print('p99         ', p99)
print('Three sigma ', three_sigma)

p95          160000.0
p99          280000.0
Three sigma  184838.11811993475

training_window['CreditCard_Total_Limit'] = np.where(training_window['CreditCard_Total_Limit'] > p99, 
                                                          p99,
                                                         training_window['CreditCard_Total_Limit'])

training_window['CreditCard_Total_Limit'].describe()

count    139146.000000
mean      51060.982709
std       56753.331259
min           0.000000
25%           0.000000
50%       40000.000000
75%       64000.000000
max      280000.000000
Name: CreditCard_Total_Limit, dtype: float64

# plot a distribution plot
sns.displot(training_window['CreditCard_Total_Limit'], kind='kde')

# display the plot
plt.show()

p95 = training_window['CreditCard_Total_Spending'].quantile(0.95)
p99 = training_window['CreditCard_Total_Spending'].quantile(0.99)
three_sigma = 3 * training_window['CreditCard_Total_Spending'].std()

print('p95         ', p95)
print('p99         ', p99)
print('Three sigma ', three_sigma)

p95          17131.6725
p99          33148.23549999999
Three sigma  21870.01520929554

training_window['CreditCard_Total_Spending'] = np.where(training_window['CreditCard_Total_Spending'] > p99, 
                                                          p99,
                                                         training_window['CreditCard_Total_Spending'])

training_window['CreditCard_Total_Spending'].describe()

count    139146.000000
mean       4076.655867
std        6249.673704
min      -30164.770000
25%           0.000000
50%        1482.710000
75%        5656.147500
max       33148.235500
Name: CreditCard_Total_Spending, dtype: float64

# plot a distribution plot
sns.displot(training_window['CreditCard_Total_Spending'], kind='kde')

# display the plot
plt.show()

p95 = training_window['CreditCard_Spending_1_Installment'].quantile(0.95)
p99 = training_window['CreditCard_Spending_1_Installment'].quantile(0.99)
np99 = -training_window['CreditCard_Spending_1_Installment'].quantile(0.99)
three_sigma = 3 * training_window['CreditCard_Spending_1_Installment'].std()

print('p95         ', p95)
print('p99         ', p99)
print('np99         ', np99)
print('Three sigma ', three_sigma)

p95          7739.59
p99          19681.75649999997
np99          -19681.75649999997
Three sigma  12897.110532905726

training_window['CreditCard_Spending_1_Installment'] = np.where(training_window['CreditCard_Spending_1_Installment'] > p99, 
                                                          p99,
                                                         training_window['CreditCard_Spending_1_Installment'])

training_window['CreditCard_Spending_1_Installment'] = np.where(training_window['CreditCard_Spending_1_Installment'] < np99, 
                                                          np99,
                                                         training_window['CreditCard_Spending_1_Installment'])

training_window['CreditCard_Spending_1_Installment'].describe()

count    139146.000000
mean       1268.888989
std        3255.660124
min      -19681.756500
25%           0.000000
50%           0.000000
75%         656.180000
max       19681.756500
Name: CreditCard_Spending_1_Installment, dtype: float64

# plot a distribution plot
sns.displot(training_window['CreditCard_Spending_1_Installment'], kind='kde')

# display the plot
plt.show()

p95 = training_window['CreditCard_Spending_CrossBoarder'].quantile(0.95)
p99 = training_window['CreditCard_Spending_CrossBoarder'].quantile(0.99)
three_sigma = 3 * training_window['CreditCard_Spending_CrossBoarder'].std()

print('p95         ', p95)
print('p99         ', p99)
print('Three sigma ', three_sigma)

p95          0.99
p99          143.6204999999987
Three sigma  212.90138519851982

training_window['CreditCard_Spending_CrossBoarder'] = np.where(training_window['CreditCard_Spending_CrossBoarder'] > p99, 
                                                          p99,
                                                         training_window['CreditCard_Spending_CrossBoarder'])

training_window['CreditCard_Spending_CrossBoarder'].describe()

count    139146.000000
mean          2.589213
std          16.595921
min           0.000000
25%           0.000000
50%           0.000000
75%           0.000000
max         143.620500
Name: CreditCard_Spending_CrossBoarder, dtype: float64

# plot a distribution plot
sns.displot(training_window['CreditCard_Spending_CrossBoarder'], kind='kde')

# display the plot
plt.show()

p95 = training_window['CreditCard_Spending_Aut_Debits'].quantile(0.95)
p99 = training_window['CreditCard_Spending_Aut_Debits'].quantile(0.99)
three_sigma = 3 * training_window['CreditCard_Spending_Aut_Debits'].std()

print('p95         ', p95)
print('p99         ', p99)
print('Three sigma ', three_sigma)

p95          5619.7275
p99          12510.912499999991
Three sigma  7806.871328247001

training_window['CreditCard_Spending_Aut_Debits'] = np.where(training_window['CreditCard_Spending_Aut_Debits'] > p99, 
                                                          p99,
                                                         training_window['CreditCard_Spending_Aut_Debits'])

training_window['CreditCard_Spending_Aut_Debits'].describe()

count    139146.000000
mean       1033.300702
std        2209.574033
min       -9476.360000
25%           0.000000
50%           0.000000
75%         947.745000
max       12510.912500
Name: CreditCard_Spending_Aut_Debits, dtype: float64

# plot a distribution plot
sns.displot(training_window['CreditCard_Spending_Aut_Debits'], kind='kde')

# display the plot
plt.show()

## quitando outliers Variable: CreditCard_Revolving

p95 = training_window['CreditCard_Revolving'].quantile(0.95)
p99 = training_window['CreditCard_Revolving'].quantile(0.99)
np99 = -training_window['CreditCard_Revolving'].quantile(0.99)
three_sigma = 3 * training_window['CreditCard_Revolving'].std()

print('p95         ', p95)
print('p99         ', p99)
print('np99         ', np99)
print('Three sigma ', three_sigma)

p95          17291.8975
p99          36175.25699999951
np99          -36175.25699999951
Three sigma  23840.512630865414

training_window['CreditCard_Revolving'] = np.where(training_window['CreditCard_Revolving'] > p99, 
                                                          p99,
                                                         training_window['CreditCard_Revolving'])

training_window['CreditCard_Revolving'] = np.where(training_window['CreditCard_Revolving'] < np99, 
                                                          np99,
                                                         training_window['CreditCard_Revolving'])
training_window['CreditCard_Revolving'].describe()

count    139146.000000
mean       2040.762848
std        6810.370574
min      -36175.257000
25%           0.000000
50%           0.000000
75%           0.000000
max       36175.257000
Name: CreditCard_Revolving, dtype: float64

# plot a distribution plot
sns.displot(training_window['CreditCard_Revolving'], kind='kde')

# display the plot
plt.show()

training_window

columnas = ['CreditCard_Premium','CreditCard_Active','CreditCard_CoBranding','Loan_Active',
            'Mortgage_Active', 'SavingAccount_Active_ARG_Salary','SavingAccount_Active_ARG','SavingAccount_Active_DOLLAR'
           ,'DebitCard_Active','Investment_Active','Package_Active','Insurance_Life'
           ,'Insurance_Home','Insurance_Accidents','Insurance_Mobile','Insurance_ATM','Insurance_Unemployment','Mobile','Email']

for columna in columnas:
    training_window[columna] = np.where(training_window[columna] == 'Yes',1,0)
#----------    

training_window['Sex'] = np.where(training_window['Sex'] == 'F', 0, 1)

#---------------

di = { 
 "Entre 40 y 49 años"          : 40,
 "Entre 30 y 39 años"                 : 30,
    "Entre 50 y 59 años"                 : 50,
    "Entre 60 y 64 años"                 : 60,
    "Entre 65 y 69 años"                 : 65,
    "Entre 18 y 29 años"                 : 18,
    "Mayor a 70 años"                 : 70,
    "Menor a 18 años"                 : 17,
 }
training_window.Client_Age_grp = training_window.Client_Age_grp.map(di)

training_window['Client_Age_grp'].value_counts()

Client_Age_grp
40    38313
30    34063
50    30588
60    12282
65     9685
18     7509
70     6701
17        5
Name: count, dtype: int64

training_window

training_window = pd.get_dummies(training_window, columns = ['Region'])

training_window

columnas = ['Region_AMBA Resto','Region_BUENOS AIRES','Region_CABA Centro/Norte',
            'Region_REGION CENTRO', 'Region_REGION CUYO','Region_REGION NORTE GRANDE ARGENTINO','Region_REGION PATAGONICA']

for columna in columnas:
    training_window[columna] = np.where(training_window[columna] == True,1,0)
    
training_window

training_window.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 139146 entries, 0 to 139145
Data columns (total 85 columns):
 #   Column                                         Non-Null Count   Dtype  
---  ------                                         --------------   -----  
 0   client_id                                      139146 non-null  int32  
 1   Target                                         139146 non-null  float64
 2   Month                                          139146 non-null  object 
 3   First_product_dt                               139146 non-null  object 
 4   Last_product_dt                                139146 non-null  object 
 5   CreditCard_Premium                             139146 non-null  int32  
 6   CreditCard_Active                              139146 non-null  int32  
 7   CreditCard_CoBranding                          139146 non-null  int32  
 8   Loan_Active                                    139146 non-null  int32  
 9   Mortgage_Active                                139146 non-null  int32  
 10  SavingAccount_Active_ARG_Salary                139146 non-null  int32  
 11  SavingAccount_Active_ARG                       139146 non-null  int32  
 12  SavingAccount_Active_DOLLAR                    139146 non-null  int32  
 13  DebitCard_Active                               139146 non-null  int32  
 14  Investment_Active                              139146 non-null  int32  
 15  Package_Active                                 139146 non-null  int32  
 16  Insurance_Life                                 139146 non-null  int32  
 17  Insurance_Home                                 139146 non-null  int32  
 18  Insurance_Accidents                            139146 non-null  int32  
 19  Insurance_Mobile                               139146 non-null  int32  
 20  Insurance_ATM                                  139146 non-null  int32  
 21  Insurance_Unemployment                         139146 non-null  int32  
 22  Sex                                            139146 non-null  int32  
 23  Client_Age_grp                                 139146 non-null  int64  
 24  SavingAccount_Balance_FirstDate                139146 non-null  float64
 25  SavingAccount_Balance_LastDate                 139146 non-null  float64
 26  SavingAccount_Balance_Average                  139146 non-null  float64
 27  SavingAccount_Days_with_use                    139146 non-null  float64
 28  SavingAccount_Days_with_Credits                139146 non-null  float64
 29  SavingAccount_Days_with_Debits                 139146 non-null  float64
 30  SavingAccount_Salary_Payment_Transactions      139146 non-null  float64
 31  SavingAccount_Transfer_In_Transactions         139146 non-null  float64
 32  SavingAccount_ATM_Extraction_Transactions      139146 non-null  float64
 33  SavingAccount_Service_Payment_Transactions     139146 non-null  float64
 34  SavingAccount_CreditCard_Payment_Transactions  139146 non-null  float64
 35  SavingAccount_Transfer_Out_Transactions        139146 non-null  float64
 36  SavingAccount_DebitCard_Spend_Transactions     139146 non-null  float64
 37  SavingAccount_Transactions_Transactions        139146 non-null  float64
 38  SavingAccount_Credits_Transactions             139146 non-null  float64
 39  SavingAccount_Debits_Transactions              139146 non-null  float64
 40  SavingAccount_Salary_Payment_Amount            139146 non-null  float64
 41  SavingAccount_Transfer_In_Amount               139146 non-null  float64
 42  SavingAccount_ATM_Extraction_Amount            139146 non-null  float64
 43  SavingAccount_Service_Payment_Amount           139146 non-null  float64
 44  SavingAccount_CreditCard_Payment_Amount        139146 non-null  float64
 45  SavingAccount_Transfer_Out_Amount              139146 non-null  float64
 46  SavingAccount_DebitCard_Spend_Amount           139146 non-null  float64
 47  SavingAccount_Total_Amount                     139146 non-null  float64
 48  SavingAccount_Credits_Amounts                  139146 non-null  float64
 49  SavingAccount_Debits_Amounts                   139146 non-null  float64
 50  Operations_Bank                                139146 non-null  float64
 51  Operations_Terminal                            139146 non-null  float64
 52  Operations_HomeBanking                         139146 non-null  float64
 53  Operations_Mobile                              139146 non-null  float64
 54  Operations_Ivr                                 139146 non-null  float64
 55  Operations_Telemarketer                        139146 non-null  float64
 56  Operations_ATM                                 139146 non-null  float64
 57  CreditCard_Balance_ARG                         139146 non-null  float64
 58  CreditCard_Balance_DOLLAR                      139146 non-null  float64
 59  CreditCard_Total_Limit                         139146 non-null  float64
 60  CreditCard_Total_Spending                      139146 non-null  float64
 61  CreditCard_Spending_1_Installment              139146 non-null  float64
 62  CreditCard_Spending_Installments               139146 non-null  float64
 63  CreditCard_Spending_CrossBoarder               139146 non-null  float64
 64  CreditCard_Spending_Aut_Debits                 139146 non-null  float64
 65  CreditCard_Revolving                           139146 non-null  float64
 66  CreditCard_Payment_Aut_Debit                   139146 non-null  float64
 67  CreditCard_Payment_External                    139146 non-null  float64
 68  CreditCard_Payment_Cash                        139146 non-null  float64
 69  CreditCard_Payment_Web                         139146 non-null  float64
 70  CreditCard_Payment_ATM                         139146 non-null  float64
 71  CreditCard_Payment_TAS                         139146 non-null  float64
 72  Investment_Numbers                             139146 non-null  float64
 73  Mobile                                         139146 non-null  int32  
 74  Email                                          139146 non-null  int32  
 75  CreditCard_Product                             139146 non-null  object 
 76  cantidad_meses                                 139146 non-null  int64  
 77  TGT                                            139146 non-null  float64
 78  Region_AMBA Resto                              139146 non-null  int32  
 79  Region_BUENOS AIRES                            139146 non-null  int32  
 80  Region_CABA Centro/Norte                       139146 non-null  int32  
 81  Region_REGION CENTRO                           139146 non-null  int32  
 82  Region_REGION CUYO                             139146 non-null  int32  
 83  Region_REGION NORTE GRANDE ARGENTINO           139146 non-null  int32  
 84  Region_REGION PATAGONICA                       139146 non-null  int32  
dtypes: float64(51), int32(28), int64(2), object(4)
memory usage: 75.4+ MB

identity_features = training_window[training_window.Month == '2019-01-01'][['client_id',
                                                                             'Target',
                                                                             'Month',
                                                                             'First_product_dt',
                                                                             'Last_product_dt',
                                                                             'CreditCard_Premium',
                                                                             'CreditCard_Active',
                                                                             'Loan_Active',
                                                                             'Mortgage_Active',
                                                                             'DebitCard_Active',
                                                                             'Investment_Active',
                                                                             'Sex',
                                                                             'Client_Age_grp',
                                                                             'Mobile',
                                                                             'Email',
                                                                             'CreditCard_Product',
                                                                             'Region_AMBA Resto',
                                                                             'Region_BUENOS AIRES',
                                                                             'Region_CABA Centro/Norte',
                                                                             'Region_REGION CENTRO',
                                                                             'Region_REGION CUYO',
                                                                             'Region_REGION NORTE GRANDE ARGENTINO',
                                                                             'Region_REGION PATAGONICA',
                                                                             'SavingAccount_Active_ARG_Salary',
                                                                             'SavingAccount_Active_ARG',
                                                                             'SavingAccount_Active_DOLLAR',
                                                                             'SavingAccount_Days_with_Credits',
                               'SavingAccount_Days_with_Debits',
                               'SavingAccount_Salary_Payment_Transactions',
                               'SavingAccount_Transfer_In_Transactions',
                               'SavingAccount_ATM_Extraction_Transactions',
                               'SavingAccount_CreditCard_Payment_Transactions',
                               'SavingAccount_Transfer_Out_Transactions',
                               'SavingAccount_DebitCard_Spend_Transactions',
                               'SavingAccount_Transactions_Transactions',
                               'SavingAccount_Credits_Transactions',
                               'SavingAccount_Debits_Transactions',
                               'SavingAccount_Salary_Payment_Amount',
                               'SavingAccount_Transfer_In_Amount',
                               'SavingAccount_ATM_Extraction_Amount',
                               'SavingAccount_CreditCard_Payment_Amount',
                               'SavingAccount_Transfer_Out_Amount',
                               'SavingAccount_DebitCard_Spend_Amount',
                               'SavingAccount_Total_Amount',
                               'SavingAccount_Credits_Amounts',
                               'SavingAccount_Debits_Amounts',
                               'Operations_HomeBanking',
                               'Operations_Mobile',
                               'CreditCard_Balance_ARG',
                               'CreditCard_Balance_DOLLAR',
                               'CreditCard_Total_Limit',
                               'CreditCard_Total_Spending',
                               'CreditCard_Spending_1_Installment',
                               'CreditCard_Spending_CrossBoarder',
                               'CreditCard_Spending_Aut_Debits',
                               'CreditCard_Revolving',
                                'TGT']].copy()

identity_features

dateColumns = ['Month','First_product_dt','Last_product_dt']

identity_features.drop(columns=dateColumns, inplace=True)

identity_features

identity_features.CreditCard_Product.value_counts()

CreditCard_Product
No                8426
J55660104XX012    8262
J55660202XX012    5759
J55660102XX012     402
J55660702XX012     249
J55661002XX012      62
J55660124XX012      30
J55660123XX012       1
Name: count, dtype: int64

CreditCard_Product_Transform = identity_features[['client_id','CreditCard_Product','TGT']].groupby('CreditCard_Product').agg(['sum',np.count_nonzero])
CreditCard_Product_Transform.columns = ['_'.join(x) for x in np.array(CreditCard_Product_Transform.columns)]

CreditCard_Product_Transform['Porcent_TGT'] = CreditCard_Product_Transform['TGT_sum']/CreditCard_Product_Transform['client_id_count_nonzero']

CreditCard_Product_Transform

di = { 
 "No"                     : 6,
 "J55660102XX012"         : 35,
 "J55660104XX012"         : 48,
 "J55660123XX012"         : 0,
 "J55660124XX012"         : 35,
 "J55660202XX012"         : 35,
 "J55660702XX012"         : 48,
 "J55661002XX012"         : 48,
 }
identity_features.CreditCard_Product = identity_features.CreditCard_Product.map(di)

identity_features.CreditCard_Product.value_counts()

CreditCard_Product
48    8573
6     8426
35    6191
0        1
Name: count, dtype: int64

columns=[x for x in training_window if(x in ([                                             
                               'SavingAccount_Days_with_Credits',
                               'SavingAccount_Days_with_Debits',
                               'SavingAccount_Salary_Payment_Transactions',
                               'SavingAccount_Transfer_In_Transactions',
                               'SavingAccount_ATM_Extraction_Transactions',
                               'SavingAccount_CreditCard_Payment_Transactions',
                               'SavingAccount_Transfer_Out_Transactions',
                               'SavingAccount_DebitCard_Spend_Transactions',
                               'SavingAccount_Transactions_Transactions',
                               'SavingAccount_Credits_Transactions',
                               'SavingAccount_Debits_Transactions',
                               'SavingAccount_Salary_Payment_Amount',
                               'SavingAccount_Transfer_In_Amount',
                               'SavingAccount_ATM_Extraction_Amount',
                               'SavingAccount_CreditCard_Payment_Amount',
                               'SavingAccount_Transfer_Out_Amount',
                               'SavingAccount_DebitCard_Spend_Amount',
                               'SavingAccount_Total_Amount',
                               'SavingAccount_Credits_Amounts',
                               'SavingAccount_Debits_Amounts',
                               'Operations_HomeBanking',
                               'Operations_Mobile',
                               'CreditCard_Balance_ARG',
                               'CreditCard_Balance_DOLLAR',
                               'CreditCard_Total_Limit',
                               'CreditCard_Total_Spending',
                               'CreditCard_Spending_1_Installment',
                               'CreditCard_Spending_CrossBoarder',
                               'CreditCard_Spending_Aut_Debits',
                               'CreditCard_Revolving']))]
len(columns)

30

aggregateFeatures = training_window.groupby('client_id')[columns].agg(['sum','max','min', 'mean','nunique', np.count_nonzero,'var']).reset_index()
aggregateFeatures.columns = ['_'.join(x) for x in np.array(aggregateFeatures.columns)]

aggregateFeatures.rename(columns={'client_id_':'client_id'}, inplace=True)

aggregateFeatures

Operations = training_window[['client_id','Operations_Bank','Operations_Terminal','Operations_HomeBanking',
                              'Operations_Mobile','Operations_Ivr','Operations_Telemarketer','Operations_ATM']]

Operations['TotalOperations'] = Operations['Operations_Bank'] + Operations['Operations_Terminal'] \
                                + Operations['Operations_HomeBanking'] + Operations['Operations_Mobile'] \
                                + Operations['Operations_Ivr'] + Operations['Operations_Telemarketer'] \
                                + Operations['Operations_ATM']



Operations_Aggregate = Operations.groupby('client_id').agg('sum').reset_index()

Operations_Aggregate['Porcent_Operations_HomeBanking'] = np.where(Operations_Aggregate['TotalOperations'] == 0,0,Operations_Aggregate['Operations_HomeBanking']/Operations_Aggregate['TotalOperations'])
Operations_Aggregate['Porcent_Operations_Mobile'] = np.where(Operations_Aggregate['TotalOperations'] == 0,0,Operations_Aggregate['Operations_Mobile']/Operations_Aggregate['TotalOperations'])

Operations_Aggregate

C:\Users\tutem\AppData\Local\Temp\ipykernel_22168\2919754844.py:4: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  Operations['TotalOperations'] = Operations['Operations_Bank'] + Operations['Operations_Terminal'] \

Insurances = training_window[['client_id','Insurance_Life','Insurance_Home','Insurance_Accidents',
                              'Insurance_Mobile','Insurance_ATM','Insurance_Unemployment']]

Insurances['TotalInsurances'] = Insurances['Insurance_Life'] + Insurances['Insurance_Home'] \
                                + Insurances['Insurance_Accidents'] + Insurances['Insurance_Mobile'] \
                                + Insurances['Insurance_ATM'] + Insurances['Insurance_Unemployment']



Insurances_Aggregate = Insurances.groupby('client_id').agg('sum').reset_index()

Insurances_Aggregate['Porcent_Total_Insurances'] = Insurances_Aggregate['TotalInsurances'] / 5
#Insurances_Aggregate = Insurances_Aggregate[['client_id','TotalInsurances','Porcent_Total_Insurances']]

Insurances_Aggregate

C:\Users\tutem\AppData\Local\Temp\ipykernel_22168\4288838385.py:4: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  Insurances['TotalInsurances'] = Insurances['Insurance_Life'] + Insurances['Insurance_Home'] \

ABT = identity_features.merge(aggregateFeatures, how='inner', on='client_id')\
        .merge(Operations_Aggregate, how='inner', on='client_id')\
        .merge(Insurances_Aggregate, how='inner', on='client_id')
ABT.drop(columns=['Target'], inplace=True)
ABT

ABT.describe()

x = pd.DataFrame(ABT.describe().T)
x.columns

delete = x[x['min']==x['max']].reset_index()

print('ABT min/max: ', ABT.shape)
ABT.drop(delete['index'], axis=1, inplace=True)
print('ABT: ', ABT.shape)

ABT min/max:  (23191, 281)
ABT:  (23191, 281)

#to scale the data using z-score 
from sklearn.preprocessing import StandardScaler
cols = [x for x in ABT.columns if (x.startswith('CreditCard_Spending')) ]
print(cols)
scaler = StandardScaler()
data_scaled = scaler.fit_transform(ABT[cols])

type(data_scaled)

['CreditCard_Spending_1_Installment', 'CreditCard_Spending_CrossBoarder', 'CreditCard_Spending_Aut_Debits', 'CreditCard_Spending_1_Installment_sum', 'CreditCard_Spending_1_Installment_max', 'CreditCard_Spending_1_Installment_min', 'CreditCard_Spending_1_Installment_mean', 'CreditCard_Spending_1_Installment_nunique', 'CreditCard_Spending_1_Installment_count_nonzero', 'CreditCard_Spending_1_Installment_var', 'CreditCard_Spending_CrossBoarder_sum', 'CreditCard_Spending_CrossBoarder_max', 'CreditCard_Spending_CrossBoarder_min', 'CreditCard_Spending_CrossBoarder_mean', 'CreditCard_Spending_CrossBoarder_nunique', 'CreditCard_Spending_CrossBoarder_count_nonzero', 'CreditCard_Spending_CrossBoarder_var', 'CreditCard_Spending_Aut_Debits_sum', 'CreditCard_Spending_Aut_Debits_max', 'CreditCard_Spending_Aut_Debits_min', 'CreditCard_Spending_Aut_Debits_mean', 'CreditCard_Spending_Aut_Debits_nunique', 'CreditCard_Spending_Aut_Debits_count_nonzero', 'CreditCard_Spending_Aut_Debits_var']

numpy.ndarray

data_scaled = pd.DataFrame(data_scaled, columns=ABT[cols].columns)

data_scaled.head(5)

data_scaled.describe()

#Importing PCA and TSNE
from sklearn.decomposition import PCA

#Defining the number of principal components to generate 
n = data_scaled.shape[1]

#Finding principal components for the data
pca1 = PCA(n_components=n, random_state=1)
data_pca = pd.DataFrame(pca1.fit_transform(data_scaled))

#The percentage of variance explained by each principal component
exp_var1 = pca1.explained_variance_ratio_

pca1.explained_variance_ratio_.cumsum()

array([0.38312432, 0.56622212, 0.6940415 , 0.74982331, 0.79421735,
       0.83520993, 0.87372543, 0.90582188, 0.93159319, 0.95309944,
       0.96551444, 0.97468403, 0.98155217, 0.98748567, 0.99094997,
       0.99363639, 0.99602674, 0.99733536, 0.99841912, 0.99946476,
       1.        , 1.        , 1.        , 1.        ])

import matplotlib.pyplot as plt
import seaborn as sns

# visulaize the explained variance by individual components
plt.figure(figsize = (10,10))
plt.plot(range(1,n+1), pca1.explained_variance_ratio_.cumsum(), marker = 'o', linestyle = '--')
plt.title("Explained Variances by Components")
plt.xlabel("Number of Components")
plt.ylabel("Cumulative Explained Variance")

Text(0, 0.5, 'Cumulative Explained Variance')

# find the least number of components that can explain more than 80% variance
sum = 0
for ix, i in enumerate(exp_var1):
  sum = sum + i
  if(sum>0.80):
    print("Number of PCs that explain at least 80% variance: ", ix+1)
    break

Number of PCs that explain at least 80% variance:  6

# find the least number of components that can explain more than 70% variance
sum = 0
for ix, i in enumerate(exp_var1):
  sum = sum + i
  if(sum>0.70):
    print("Number of PCs that explain at least 70% variance: ", ix+1)
    break

Number of PCs that explain at least 70% variance:  4

cols = ['PC1', 'PC2', 'PC3', 'PC4']

pc1 = pd.DataFrame(np.round(pca1.components_.T[:, 0:4],2), index=data_scaled.columns, columns=cols)

def color_high(val):
    if val <= -0.25: # you can decide any value as per your understanding
        return 'background: pink'
    elif val >= 0.25:
        return 'background: skyblue'   
    
pc1.style.applymap(color_high)

ABT.corr()

# if Inplace is False, return a copy, default drop(inplace=False)
corr_Matrix = ABT.drop(['client_id','TGT'], axis=1).corr().abs()
corr_Matrix

sns.heatmap(corr_Matrix, annot=True)

<Axes: >

upper = corr_Matrix.where(np.triu(np.ones(corr_Matrix.shape),k=1).astype(bool)).fillna(0)

## features to drop, because correlation is up to 0.8
to_drop = [column for column in upper.columns if any(upper[column] > 0.8)]
#len(to_drop)

to_drop

['CreditCard_Product',
 'SavingAccount_Active_ARG',
 'SavingAccount_Days_with_Debits',
 'SavingAccount_Transactions_Transactions',
 'SavingAccount_Credits_Transactions',
 'SavingAccount_Debits_Transactions',
 'SavingAccount_Salary_Payment_Amount',
 'SavingAccount_Transfer_In_Amount',
 'SavingAccount_ATM_Extraction_Amount',
 'SavingAccount_DebitCard_Spend_Amount',
 'SavingAccount_Credits_Amounts',
 'SavingAccount_Debits_Amounts',
 'CreditCard_Spending_1_Installment',
 'CreditCard_Spending_CrossBoarder',
 'SavingAccount_Days_with_Credits_sum',
 'SavingAccount_Days_with_Credits_max',
 'SavingAccount_Days_with_Credits_min',
 'SavingAccount_Days_with_Credits_mean',
 'SavingAccount_Days_with_Credits_nunique',
 'SavingAccount_Days_with_Debits_sum',
 'SavingAccount_Days_with_Debits_max',
 'SavingAccount_Days_with_Debits_min',
 'SavingAccount_Days_with_Debits_mean',
 'SavingAccount_Days_with_Debits_count_nonzero',
 'SavingAccount_Salary_Payment_Transactions_sum',
 'SavingAccount_Salary_Payment_Transactions_max',
 'SavingAccount_Salary_Payment_Transactions_min',
 'SavingAccount_Salary_Payment_Transactions_mean',
 'SavingAccount_Salary_Payment_Transactions_nunique',
 'SavingAccount_Salary_Payment_Transactions_count_nonzero',
 'SavingAccount_Salary_Payment_Transactions_var',
 'SavingAccount_Transfer_In_Transactions_sum',
 'SavingAccount_Transfer_In_Transactions_max',
 'SavingAccount_Transfer_In_Transactions_mean',
 'SavingAccount_Transfer_In_Transactions_nunique',
 'SavingAccount_Transfer_In_Transactions_count_nonzero',
 'SavingAccount_Transfer_In_Transactions_var',
 'SavingAccount_ATM_Extraction_Transactions_sum',
 'SavingAccount_ATM_Extraction_Transactions_max',
 'SavingAccount_ATM_Extraction_Transactions_min',
 'SavingAccount_ATM_Extraction_Transactions_mean',
 'SavingAccount_ATM_Extraction_Transactions_count_nonzero',
 'SavingAccount_ATM_Extraction_Transactions_var',
 'SavingAccount_CreditCard_Payment_Transactions_sum',
 'SavingAccount_CreditCard_Payment_Transactions_max',
 'SavingAccount_CreditCard_Payment_Transactions_min',
 'SavingAccount_CreditCard_Payment_Transactions_mean',
 'SavingAccount_CreditCard_Payment_Transactions_nunique',
 'SavingAccount_CreditCard_Payment_Transactions_count_nonzero',
 'SavingAccount_CreditCard_Payment_Transactions_var',
 'SavingAccount_Transfer_Out_Transactions_sum',
 'SavingAccount_Transfer_Out_Transactions_max',
 'SavingAccount_Transfer_Out_Transactions_min',
 'SavingAccount_Transfer_Out_Transactions_mean',
 'SavingAccount_Transfer_Out_Transactions_nunique',
 'SavingAccount_Transfer_Out_Transactions_count_nonzero',
 'SavingAccount_Transfer_Out_Transactions_var',
 'SavingAccount_DebitCard_Spend_Transactions_sum',
 'SavingAccount_DebitCard_Spend_Transactions_max',
 'SavingAccount_DebitCard_Spend_Transactions_min',
 'SavingAccount_DebitCard_Spend_Transactions_mean',
 'SavingAccount_DebitCard_Spend_Transactions_nunique',
 'SavingAccount_DebitCard_Spend_Transactions_count_nonzero',
 'SavingAccount_DebitCard_Spend_Transactions_var',
 'SavingAccount_Transactions_Transactions_sum',
 'SavingAccount_Transactions_Transactions_max',
 'SavingAccount_Transactions_Transactions_min',
 'SavingAccount_Transactions_Transactions_mean',
 'SavingAccount_Transactions_Transactions_nunique',
 'SavingAccount_Transactions_Transactions_count_nonzero',
 'SavingAccount_Credits_Transactions_sum',
 'SavingAccount_Credits_Transactions_max',
 'SavingAccount_Credits_Transactions_min',
 'SavingAccount_Credits_Transactions_mean',
 'SavingAccount_Credits_Transactions_nunique',
 'SavingAccount_Credits_Transactions_count_nonzero',
 'SavingAccount_Debits_Transactions_sum',
 'SavingAccount_Debits_Transactions_max',
 'SavingAccount_Debits_Transactions_min',
 'SavingAccount_Debits_Transactions_mean',
 'SavingAccount_Debits_Transactions_nunique',
 'SavingAccount_Debits_Transactions_count_nonzero',
 'SavingAccount_Debits_Transactions_var',
 'SavingAccount_Salary_Payment_Amount_sum',
 'SavingAccount_Salary_Payment_Amount_max',
 'SavingAccount_Salary_Payment_Amount_min',
 'SavingAccount_Salary_Payment_Amount_mean',
 'SavingAccount_Salary_Payment_Amount_count_nonzero',
 'SavingAccount_Transfer_In_Amount_sum',
 'SavingAccount_Transfer_In_Amount_max',
 'SavingAccount_Transfer_In_Amount_min',
 'SavingAccount_Transfer_In_Amount_mean',
 'SavingAccount_Transfer_In_Amount_nunique',
 'SavingAccount_Transfer_In_Amount_count_nonzero',
 'SavingAccount_ATM_Extraction_Amount_sum',
 'SavingAccount_ATM_Extraction_Amount_max',
 'SavingAccount_ATM_Extraction_Amount_min',
 'SavingAccount_ATM_Extraction_Amount_mean',
 'SavingAccount_ATM_Extraction_Amount_nunique',
 'SavingAccount_ATM_Extraction_Amount_count_nonzero',
 'SavingAccount_ATM_Extraction_Amount_var',
 'SavingAccount_CreditCard_Payment_Amount_sum',
 'SavingAccount_CreditCard_Payment_Amount_max',
 'SavingAccount_CreditCard_Payment_Amount_mean',
 'SavingAccount_CreditCard_Payment_Amount_nunique',
 'SavingAccount_CreditCard_Payment_Amount_count_nonzero',
 'SavingAccount_CreditCard_Payment_Amount_var',
 'SavingAccount_Transfer_Out_Amount_max',
 'SavingAccount_Transfer_Out_Amount_mean',
 'SavingAccount_Transfer_Out_Amount_count_nonzero',
 'SavingAccount_Transfer_Out_Amount_var',
 'SavingAccount_DebitCard_Spend_Amount_sum',
 'SavingAccount_DebitCard_Spend_Amount_max',
 'SavingAccount_DebitCard_Spend_Amount_min',
 'SavingAccount_DebitCard_Spend_Amount_mean',
 'SavingAccount_DebitCard_Spend_Amount_nunique',
 'SavingAccount_DebitCard_Spend_Amount_count_nonzero',
 'SavingAccount_Total_Amount_sum',
 'SavingAccount_Total_Amount_max',
 'SavingAccount_Total_Amount_min',
 'SavingAccount_Total_Amount_mean',
 'SavingAccount_Total_Amount_nunique',
 'SavingAccount_Total_Amount_count_nonzero',
 'SavingAccount_Credits_Amounts_sum',
 'SavingAccount_Credits_Amounts_max',
 'SavingAccount_Credits_Amounts_min',
 'SavingAccount_Credits_Amounts_mean',
 'SavingAccount_Credits_Amounts_nunique',
 'SavingAccount_Credits_Amounts_count_nonzero',
 'SavingAccount_Credits_Amounts_var',
 'SavingAccount_Debits_Amounts_sum',
 'SavingAccount_Debits_Amounts_max',
 'SavingAccount_Debits_Amounts_min',
 'SavingAccount_Debits_Amounts_mean',
 'SavingAccount_Debits_Amounts_nunique',
 'SavingAccount_Debits_Amounts_count_nonzero',
 'SavingAccount_Debits_Amounts_var',
 'Operations_HomeBanking_sum',
 'Operations_HomeBanking_max',
 'Operations_HomeBanking_min',
 'Operations_HomeBanking_mean',
 'Operations_HomeBanking_nunique',
 'Operations_HomeBanking_count_nonzero',
 'Operations_Mobile_sum',
 'Operations_Mobile_max',
 'Operations_Mobile_min',
 'Operations_Mobile_mean',
 'Operations_Mobile_nunique',
 'Operations_Mobile_count_nonzero',
 'CreditCard_Balance_ARG_sum',
 'CreditCard_Balance_ARG_max',
 'CreditCard_Balance_ARG_min',
 'CreditCard_Balance_ARG_mean',
 'CreditCard_Balance_ARG_nunique',
 'CreditCard_Balance_ARG_count_nonzero',
 'CreditCard_Balance_DOLLAR_max',
 'CreditCard_Balance_DOLLAR_mean',
 'CreditCard_Balance_DOLLAR_count_nonzero',
 'CreditCard_Balance_DOLLAR_var',
 'CreditCard_Total_Limit_sum',
 'CreditCard_Total_Limit_max',
 'CreditCard_Total_Limit_min',
 'CreditCard_Total_Limit_mean',
 'CreditCard_Total_Limit_count_nonzero',
 'CreditCard_Total_Spending_sum',
 'CreditCard_Total_Spending_max',
 'CreditCard_Total_Spending_min',
 'CreditCard_Total_Spending_mean',
 'CreditCard_Total_Spending_nunique',
 'CreditCard_Total_Spending_count_nonzero',
 'CreditCard_Spending_1_Installment_sum',
 'CreditCard_Spending_1_Installment_max',
 'CreditCard_Spending_1_Installment_mean',
 'CreditCard_Spending_1_Installment_count_nonzero',
 'CreditCard_Spending_1_Installment_var',
 'CreditCard_Spending_CrossBoarder_sum',
 'CreditCard_Spending_CrossBoarder_max',
 'CreditCard_Spending_CrossBoarder_mean',
 'CreditCard_Spending_CrossBoarder_nunique',
 'CreditCard_Spending_CrossBoarder_count_nonzero',
 'CreditCard_Spending_CrossBoarder_var',
 'CreditCard_Spending_Aut_Debits_sum',
 'CreditCard_Spending_Aut_Debits_max',
 'CreditCard_Spending_Aut_Debits_min',
 'CreditCard_Spending_Aut_Debits_mean',
 'CreditCard_Revolving_sum',
 'CreditCard_Revolving_max',
 'CreditCard_Revolving_mean',
 'CreditCard_Revolving_count_nonzero',
 'Operations_HomeBanking_y',
 'Operations_Mobile_y',
 'Porcent_Operations_HomeBanking',
 'Porcent_Operations_Mobile',
 'Porcent_Total_Insurances']

#drop features
ABT.drop(to_drop,axis=1, inplace=True)
ABT.shape

(23191, 87)

ABT.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 23191 entries, 0 to 23190
Data columns (total 87 columns):
 #   Column                                             Non-Null Count  Dtype  
---  ------                                             --------------  -----  
 0   client_id                                          23191 non-null  int32  
 1   CreditCard_Premium                                 23191 non-null  int32  
 2   CreditCard_Active                                  23191 non-null  int32  
 3   Loan_Active                                        23191 non-null  int32  
 4   Mortgage_Active                                    23191 non-null  int32  
 5   DebitCard_Active                                   23191 non-null  int32  
 6   Investment_Active                                  23191 non-null  int32  
 7   Sex                                                23191 non-null  int32  
 8   Client_Age_grp                                     23191 non-null  int64  
 9   Mobile                                             23191 non-null  int32  
 10  Email                                              23191 non-null  int32  
 11  Region_AMBA Resto                                  23191 non-null  int32  
 12  Region_BUENOS AIRES                                23191 non-null  int32  
 13  Region_CABA Centro/Norte                           23191 non-null  int32  
 14  Region_REGION CENTRO                               23191 non-null  int32  
 15  Region_REGION CUYO                                 23191 non-null  int32  
 16  Region_REGION NORTE GRANDE ARGENTINO               23191 non-null  int32  
 17  Region_REGION PATAGONICA                           23191 non-null  int32  
 18  SavingAccount_Active_ARG_Salary                    23191 non-null  int32  
 19  SavingAccount_Active_DOLLAR                        23191 non-null  int32  
 20  SavingAccount_Days_with_Credits                    23191 non-null  float64
 21  SavingAccount_Salary_Payment_Transactions          23191 non-null  float64
 22  SavingAccount_Transfer_In_Transactions             23191 non-null  float64
 23  SavingAccount_ATM_Extraction_Transactions          23191 non-null  float64
 24  SavingAccount_CreditCard_Payment_Transactions      23191 non-null  float64
 25  SavingAccount_Transfer_Out_Transactions            23191 non-null  float64
 26  SavingAccount_DebitCard_Spend_Transactions         23191 non-null  float64
 27  SavingAccount_CreditCard_Payment_Amount            23191 non-null  float64
 28  SavingAccount_Transfer_Out_Amount                  23191 non-null  float64
 29  SavingAccount_Total_Amount                         23191 non-null  float64
 30  Operations_HomeBanking_x                           23191 non-null  float64
 31  Operations_Mobile_x                                23191 non-null  float64
 32  CreditCard_Balance_ARG                             23191 non-null  float64
 33  CreditCard_Balance_DOLLAR                          23191 non-null  float64
 34  CreditCard_Total_Limit                             23191 non-null  float64
 35  CreditCard_Total_Spending                          23191 non-null  float64
 36  CreditCard_Spending_Aut_Debits                     23191 non-null  float64
 37  CreditCard_Revolving                               23191 non-null  float64
 38  TGT                                                23191 non-null  float64
 39  SavingAccount_Days_with_Credits_count_nonzero      23191 non-null  int64  
 40  SavingAccount_Days_with_Credits_var                23191 non-null  float64
 41  SavingAccount_Days_with_Debits_nunique             23191 non-null  int64  
 42  SavingAccount_Days_with_Debits_var                 23191 non-null  float64
 43  SavingAccount_Transfer_In_Transactions_min         23191 non-null  float64
 44  SavingAccount_ATM_Extraction_Transactions_nunique  23191 non-null  int64  
 45  SavingAccount_Transactions_Transactions_var        23191 non-null  float64
 46  SavingAccount_Credits_Transactions_var             23191 non-null  float64
 47  SavingAccount_Salary_Payment_Amount_nunique        23191 non-null  int64  
 48  SavingAccount_Salary_Payment_Amount_var            23191 non-null  float64
 49  SavingAccount_Transfer_In_Amount_var               23191 non-null  float64
 50  SavingAccount_CreditCard_Payment_Amount_min        23191 non-null  float64
 51  SavingAccount_Transfer_Out_Amount_sum              23191 non-null  float64
 52  SavingAccount_Transfer_Out_Amount_min              23191 non-null  float64
 53  SavingAccount_Transfer_Out_Amount_nunique          23191 non-null  int64  
 54  SavingAccount_DebitCard_Spend_Amount_var           23191 non-null  float64
 55  SavingAccount_Total_Amount_var                     23191 non-null  float64
 56  Operations_HomeBanking_var                         23191 non-null  float64
 57  Operations_Mobile_var                              23191 non-null  float64
 58  CreditCard_Balance_ARG_var                         23191 non-null  float64
 59  CreditCard_Balance_DOLLAR_sum                      23191 non-null  float64
 60  CreditCard_Balance_DOLLAR_min                      23191 non-null  float64
 61  CreditCard_Balance_DOLLAR_nunique                  23191 non-null  int64  
 62  CreditCard_Total_Limit_nunique                     23191 non-null  int64  
 63  CreditCard_Total_Limit_var                         23191 non-null  float64
 64  CreditCard_Total_Spending_var                      23191 non-null  float64
 65  CreditCard_Spending_1_Installment_min              23191 non-null  float64
 66  CreditCard_Spending_1_Installment_nunique          23191 non-null  int64  
 67  CreditCard_Spending_CrossBoarder_min               23191 non-null  float64
 68  CreditCard_Spending_Aut_Debits_nunique             23191 non-null  int64  
 69  CreditCard_Spending_Aut_Debits_count_nonzero       23191 non-null  int64  
 70  CreditCard_Spending_Aut_Debits_var                 23191 non-null  float64
 71  CreditCard_Revolving_min                           23191 non-null  float64
 72  CreditCard_Revolving_nunique                       23191 non-null  int64  
 73  CreditCard_Revolving_var                           23191 non-null  float64
 74  Operations_Bank                                    23191 non-null  float64
 75  Operations_Terminal                                23191 non-null  float64
 76  Operations_Ivr                                     23191 non-null  float64
 77  Operations_Telemarketer                            23191 non-null  float64
 78  Operations_ATM                                     23191 non-null  float64
 79  TotalOperations                                    23191 non-null  float64
 80  Insurance_Life                                     23191 non-null  int32  
 81  Insurance_Home                                     23191 non-null  int32  
 82  Insurance_Accidents                                23191 non-null  int32  
 83  Insurance_Mobile                                   23191 non-null  int32  
 84  Insurance_ATM                                      23191 non-null  int32  
 85  Insurance_Unemployment                             23191 non-null  int32  
 86  TotalInsurances                                    23191 non-null  int32  
dtypes: float64(49), int32(26), int64(12)
memory usage: 13.1 MB

ABT

names = [x for x in ABT.columns if (x != 'client_id') & (x != 'TGT') ]

scaler = StandardScaler(copy=True)
scaler.fit(ABT[names]) # Entrena
scaled_est = scaler.transform(ABT[names]) # Standariza el total de la base
scaled_est = pd.DataFrame(scaled_est, columns=names, index=ABT.index)

ABT.drop(names, axis=1, inplace = True)
ABT = pd.concat((ABT, scaled_est), axis=1, sort=False)

ABT.head(5)

from xgboost import XGBClassifier
from sklearn.metrics import accuracy_score, classification_report
from sklearn.model_selection import RandomizedSearchCV, GridSearchCV


target_column = 'TGT'
numerical_cols = [x for x in ABT.columns if (x != 'client_id') & (x != 'TGT')]

estimator = XGBClassifier(
    objective= 'binary:logistic',
    seed=42
)

parameters = {
    'max_depth': np.arange(6, 10, 1),
    'learning_rate': np.arange(0.01, 1, 0.05),
    'gamma': np.arange(0.1, 2, 0.1),
    'alpha': np.arange(0,10,1),
    'lambda': np.arange(0,10,1),
    'subsample': np.arange(0.1, 1, 0.1),
    'n_estimators':  np.arange(15, 20, 1)
}

#This parameter defines the number of HP points to be tested
n_HP_points_to_test = 100

grid_search = RandomizedSearchCV(
    estimator=estimator,
    param_distributions=parameters,
    n_iter= n_HP_points_to_test,
    scoring='roc_auc',
    cv=3,
    refit=True,
    verbose=False)

grid_search.fit(ABT[numerical_cols], ABT[target_column])

RandomizedSearchCV(cv=3,
                   estimator=XGBClassifier(base_score=None, booster=None,
                                           callbacks=None,
                                           colsample_bylevel=None,
                                           colsample_bynode=None,
                                           colsample_bytree=None, device=None,
                                           early_stopping_rounds=None,
                                           enable_categorical=False,
                                           eval_metric=None, feature_types=None,
                                           gamma=None, grow_policy=None,
                                           importance_type=None,
                                           interaction_constraints=None,
                                           learning_rate...
                                        'gamma': array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. , 1.1, 1.2, 1.3,
       1.4, 1.5, 1.6, 1.7, 1.8, 1.9]),
                                        'lambda': array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
                                        'learning_rate': array([0.01, 0.06, 0.11, 0.16, 0.21, 0.26, 0.31, 0.36, 0.41, 0.46, 0.51,
       0.56, 0.61, 0.66, 0.71, 0.76, 0.81, 0.86, 0.91, 0.96]),
                                        'max_depth': array([6, 7, 8, 9]),
                                        'n_estimators': array([15, 16, 17, 18, 19]),
                                        'subsample': array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])},
                   scoring='roc_auc', verbose=False)

RandomizedSearchCV(cv=3,
                   estimator=XGBClassifier(base_score=None, booster=None,
                                           callbacks=None,
                                           colsample_bylevel=None,
                                           colsample_bynode=None,
                                           colsample_bytree=None, device=None,
                                           early_stopping_rounds=None,
                                           enable_categorical=False,
                                           eval_metric=None, feature_types=None,
                                           gamma=None, grow_policy=None,
                                           importance_type=None,
                                           interaction_constraints=None,
                                           learning_rate...
                                        'gamma': array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. , 1.1, 1.2, 1.3,
       1.4, 1.5, 1.6, 1.7, 1.8, 1.9]),
                                        'lambda': array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
                                        'learning_rate': array([0.01, 0.06, 0.11, 0.16, 0.21, 0.26, 0.31, 0.36, 0.41, 0.46, 0.51,
       0.56, 0.61, 0.66, 0.71, 0.76, 0.81, 0.86, 0.91, 0.96]),
                                        'max_depth': array([6, 7, 8, 9]),
                                        'n_estimators': array([15, 16, 17, 18, 19]),
                                        'subsample': array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])},
                   scoring='roc_auc', verbose=False)

XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=None, device=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=None, grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=None, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=None, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              multi_strategy=None, n_estimators=None, n_jobs=None,
              num_parallel_tree=None, random_state=None, ...)

XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=None, device=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=None, grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=None, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=None, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              multi_strategy=None, n_estimators=None, n_jobs=None,
              num_parallel_tree=None, random_state=None, ...)

from xgboost import plot_importance
plot_importance(grid_search.best_estimator_,max_num_features=25)
plt.show()

feature_importance = grid_search.best_estimator_.get_booster().get_score()
keys = list(feature_importance.keys())
values = list(feature_importance.values())
data = pd.DataFrame(data=values, index=keys, columns=["score"]).sort_values(by="score", ascending=False)
a=data.head(50)

a.to_csv(r'result/top_50_Features.csv', sep='|')

a = pd.read_csv(r'result/top_50_Features.csv', sep='|')

a.columns = ['column', 'order']
a

def plot_numerical_data(data_col, target_col = 'TGT', _df = ABT, bins=None, qs = None):
    print (data_col)
    df = _df.copy()
    if (bins == None):
        df['rank'] = round(df[data_col].rank(pct=True) * 9)
    else:
        df['rank'] = pd.cut(df[data_col], bins)
    if (qs != None):
        df['rank'] = pd.qcut(df[data_col], qs, duplicates='drop')
    rank = pd.DataFrame(
        df.groupby(['rank'])[data_col].agg([np.min, np.max, np.average])
    ).reset_index()

    groupped = pd.DataFrame(
        df.groupby(['rank'])[['client_id']].agg('nunique', np.sum).astype('int64')
    ).reset_index()
    groupped.columns = ['rank', '# of clients']

    target = pd.DataFrame(
        df.groupby(['rank'])[[target_col]].agg(np.sum).astype('int64')
    ).reset_index() 
    target.columns = ['rank', '# of target clients']
    
    merged = rank.merge(groupped, how='left').merge(target, how='left')
    merged['target_p'] = (
        (merged['# of target clients'] / merged['# of clients']) * 100
    )
    display(merged)
    # Plot
    merged['# of clients'].plot(kind='bar')
    merged['target_p'].plot(secondary_y=True, color='g')
    plt.show()
    return merged

a

most_important=a['column']
for columna in most_important:
    plot_numerical_data(columna)

CreditCard_Balance_ARG_var

TotalOperations

CreditCard_Total_Limit

CreditCard_Total_Spending

SavingAccount_Total_Amount

Operations_Bank

Client_Age_grp

Operations_Terminal

CreditCard_Total_Spending_var

Operations_HomeBanking_var

selected_features = list(a['column'][:50])
selected_features += ['client_id', 'TGT']

selected_features

['CreditCard_Balance_ARG_var',
 'TotalOperations',
 'CreditCard_Total_Limit',
 'CreditCard_Total_Spending',
 'SavingAccount_Total_Amount',
 'Operations_Bank',
 'Client_Age_grp',
 'Operations_Terminal',
 'CreditCard_Total_Spending_var',
 'Operations_HomeBanking_var',
 'CreditCard_Revolving_min',
 'CreditCard_Total_Limit_var',
 'CreditCard_Revolving',
 'CreditCard_Spending_Aut_Debits_var',
 'SavingAccount_Transactions_Transactions_var',
 'SavingAccount_Total_Amount_var',
 'CreditCard_Balance_ARG',
 'CreditCard_Revolving_var',
 'CreditCard_Spending_Aut_Debits',
 'SavingAccount_Days_with_Credits_var',
 'SavingAccount_Credits_Transactions_var',
 'CreditCard_Balance_DOLLAR_sum',
 'TotalInsurances',
 'Operations_Telemarketer',
 'Operations_Mobile_var',
 'CreditCard_Active',
 'SavingAccount_Transfer_In_Amount_var',
 'SavingAccount_DebitCard_Spend_Transactions',
 'SavingAccount_DebitCard_Spend_Amount_var',
 'SavingAccount_Salary_Payment_Amount_var',
 'CreditCard_Spending_1_Installment_min',
 'SavingAccount_Days_with_Debits_var',
 'Operations_ATM',
 'SavingAccount_Active_ARG_Salary',
 'SavingAccount_Transfer_Out_Amount_sum',
 'SavingAccount_Salary_Payment_Transactions',
 'CreditCard_Spending_Aut_Debits_nunique',
 'Operations_HomeBanking_x',
 'DebitCard_Active',
 'Loan_Active',
 'SavingAccount_Days_with_Debits_nunique',
 'SavingAccount_CreditCard_Payment_Amount',
 'Operations_Ivr',
 'CreditCard_Spending_1_Installment_nunique',
 'Region_REGION CUYO',
 'CreditCard_Spending_Aut_Debits_count_nonzero',
 'Mobile',
 'SavingAccount_Days_with_Credits_count_nonzero',
 'Region_BUENOS AIRES',
 'Region_REGION CENTRO',
 'client_id',
 'TGT']

ABT_Model_Select = ABT[selected_features].copy()

ABT_Model_Select.shape

(23191, 52)

from sklearn.model_selection import train_test_split
X_train, X_test = train_test_split(ABT_Model_Select, test_size=0.3, random_state=42, stratify=ABT_Model_Select['TGT']);

X_train.TGT.value_counts()

TGT
0.0    11457
1.0     4776
Name: count, dtype: int64

X_test.TGT.value_counts()

TGT
0.0    4911
1.0    2047
Name: count, dtype: int64

from xgboost import XGBClassifier
from sklearn.metrics import accuracy_score, classification_report
from sklearn.model_selection import RandomizedSearchCV, GridSearchCV
from sklearn.model_selection import StratifiedKFold

target_column = 'TGT'
numerical_cols = [x for x in ABT_Model_Select.columns if (x != 'client_id') & (x != 'TGT')]

estimator = XGBClassifier(
    objective= 'binary:logistic',
    seed=42
)

parameters = {
    'max_depth': np.arange(6, 10, 1),
    'learning_rate': np.arange(0.01, 1, 0.05),
    'gamma': np.arange(0.1, 2, 0.1),
    'alpha': np.arange(0,10,1),
    'lambda': np.arange(0,10,1),
    'subsample': np.arange(0.1, 1, 0.1),
    'n_estimators': np.arange(15, 20, 1)
}

cross_val = StratifiedKFold(n_splits=3) 

grid_search = GridSearchCV(
    estimator=estimator,
    param_grid=parameters,
    scoring = 'roc_auc',
    n_jobs = 4,
    cv = cross_val,
    verbose=True
)

#This parameter defines the number of HP points to be tested
n_HP_points_to_test = 100

grid_search = RandomizedSearchCV(
    estimator=estimator,
    param_distributions=parameters,
    n_iter= n_HP_points_to_test,
    scoring='roc_auc',
    cv= cross_val,
    refit= True,
    verbose= True)

grid_search.fit(X_train[numerical_cols], X_train[target_column])

Fitting 3 folds for each of 100 candidates, totalling 300 fits

RandomizedSearchCV(cv=StratifiedKFold(n_splits=3, random_state=None, shuffle=False),
                   estimator=XGBClassifier(base_score=None, booster=None,
                                           callbacks=None,
                                           colsample_bylevel=None,
                                           colsample_bynode=None,
                                           colsample_bytree=None, device=None,
                                           early_stopping_rounds=None,
                                           enable_categorical=False,
                                           eval_metric=None, feature_types=None,
                                           gamma=None, grow_policy=None,
                                           impor...
                                        'gamma': array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. , 1.1, 1.2, 1.3,
       1.4, 1.5, 1.6, 1.7, 1.8, 1.9]),
                                        'lambda': array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
                                        'learning_rate': array([0.01, 0.06, 0.11, 0.16, 0.21, 0.26, 0.31, 0.36, 0.41, 0.46, 0.51,
       0.56, 0.61, 0.66, 0.71, 0.76, 0.81, 0.86, 0.91, 0.96]),
                                        'max_depth': array([6, 7, 8, 9]),
                                        'n_estimators': array([15, 16, 17, 18, 19]),
                                        'subsample': array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])},
                   scoring='roc_auc', verbose=True)

RandomizedSearchCV(cv=StratifiedKFold(n_splits=3, random_state=None, shuffle=False),
                   estimator=XGBClassifier(base_score=None, booster=None,
                                           callbacks=None,
                                           colsample_bylevel=None,
                                           colsample_bynode=None,
                                           colsample_bytree=None, device=None,
                                           early_stopping_rounds=None,
                                           enable_categorical=False,
                                           eval_metric=None, feature_types=None,
                                           gamma=None, grow_policy=None,
                                           impor...
                                        'gamma': array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. , 1.1, 1.2, 1.3,
       1.4, 1.5, 1.6, 1.7, 1.8, 1.9]),
                                        'lambda': array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
                                        'learning_rate': array([0.01, 0.06, 0.11, 0.16, 0.21, 0.26, 0.31, 0.36, 0.41, 0.46, 0.51,
       0.56, 0.61, 0.66, 0.71, 0.76, 0.81, 0.86, 0.91, 0.96]),
                                        'max_depth': array([6, 7, 8, 9]),
                                        'n_estimators': array([15, 16, 17, 18, 19]),
                                        'subsample': array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])},
                   scoring='roc_auc', verbose=True)

XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=None, device=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=None, grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=None, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=None, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              multi_strategy=None, n_estimators=None, n_jobs=None,
              num_parallel_tree=None, random_state=None, ...)

XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=None, device=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=None, grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=None, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=None, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              multi_strategy=None, n_estimators=None, n_jobs=None,
              num_parallel_tree=None, random_state=None, ...)

best_parameters = grid_search.best_estimator_

best_parameters.get_xgb_params()

{'objective': 'binary:logistic',
 'base_score': None,
 'booster': None,
 'colsample_bylevel': None,
 'colsample_bynode': None,
 'colsample_bytree': None,
 'device': None,
 'eval_metric': None,
 'gamma': 1.7000000000000002,
 'grow_policy': None,
 'interaction_constraints': None,
 'learning_rate': 0.26,
 'max_bin': None,
 'max_cat_threshold': None,
 'max_cat_to_onehot': None,
 'max_delta_step': None,
 'max_depth': 7,
 'max_leaves': None,
 'min_child_weight': None,
 'monotone_constraints': None,
 'multi_strategy': None,
 'n_jobs': None,
 'num_parallel_tree': None,
 'random_state': None,
 'reg_alpha': None,
 'reg_lambda': None,
 'sampling_method': None,
 'scale_pos_weight': None,
 'subsample': 0.8,
 'tree_method': None,
 'validate_parameters': None,
 'verbosity': None,
 'seed': 42,
 'lambda': 0,
 'alpha': 9}

#create model
model = XGBClassifier(objective='binary:logistic',
                      booster='gbtree',
                      seed=42,
                      gamma=1.7000000000000002,
                      learning_rate=0.26,
                      max_depth=7,
                      subsample=0.8,
                      n_estimators=19,
                     alpha=9,
                                         )

# fit model
model.fit(X_train[numerical_cols], X_train[target_column])

XGBClassifier(alpha=9, base_score=None, booster='gbtree', callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=None, device=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=1.7000000000000002, grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=0.26, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=7, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              multi_strategy=None, n_estimators=19, n_jobs=None,
              num_parallel_tree=None, ...)

XGBClassifier(alpha=9, base_score=None, booster='gbtree', callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=None, device=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=1.7000000000000002, grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=0.26, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=7, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              multi_strategy=None, n_estimators=19, n_jobs=None,
              num_parallel_tree=None, ...)

from sklearn.metrics import accuracy_score, classification_report
accuracy = accuracy_score(X_train[target_column], model.predict(X_train[numerical_cols])) * 100
print("Accuracy of Model: ",accuracy)
print(classification_report(X_train[target_column], model.predict(X_train[numerical_cols])))
pd.crosstab(X_train[target_column], model.predict(X_train[numerical_cols]))

Accuracy of Model:  81.12486909382123
              precision    recall  f1-score   support

         0.0       0.84      0.90      0.87     11457
         1.0       0.72      0.59      0.65      4776

    accuracy                           0.81     16233
   macro avg       0.78      0.75      0.76     16233
weighted avg       0.80      0.81      0.81     16233

y_pred = model.predict(X_test[numerical_cols])

from sklearn.metrics import accuracy_score, classification_report
accuracy = accuracy_score(X_test[target_column], y_pred) * 100
print("Accuracy of Model: ",accuracy)
print(classification_report(X_test[target_column], y_pred))
pd.crosstab(X_test[target_column], y_pred)

Accuracy of Model:  79.04570278815751
              precision    recall  f1-score   support

         0.0       0.82      0.90      0.86      4911
         1.0       0.68      0.54      0.60      2047

    accuracy                           0.79      6958
   macro avg       0.75      0.72      0.73      6958
weighted avg       0.78      0.79      0.78      6958

# results..
from sklearn.metrics import mean_squared_error

probabilities_train = model.predict_proba(X_train[numerical_cols])
probabilities       = model.predict_proba(X_test[numerical_cols])

y_pred = model.predict(X_test[numerical_cols])

a = pd.DataFrame(X_train[['client_id', target_column]], columns=['TGT', 'idx'])
a = a.reset_index()
b = pd.DataFrame(probabilities_train[:,1], columns=['Prob1'])

result = pd.concat([a, b], axis=1)

result['porc'] = result['Prob1'].rank(pct=True) * 100

len(probabilities[:,1])

result.loc[result['porc'].between(0, 10, inclusive='neither'), 'decil'] = '10'
result.loc[result['porc'].between(10, 20, inclusive='both'), 'decil'] = '9'
result.loc[result['porc'].between(20, 30, inclusive='neither'), 'decil'] = '8'
result.loc[result['porc'].between(30, 40, inclusive='both'), 'decil'] = '7'
result.loc[result['porc'].between(40, 50, inclusive='neither'), 'decil'] = '6'
result.loc[result['porc'].between(50, 60, inclusive='both'), 'decil'] = '5'
result.loc[result['porc'].between(60, 70, inclusive='neither'), 'decil'] = '4'
result.loc[result['porc'].between(70, 80, inclusive='both'), 'decil'] = '3'
result.loc[result['porc'].between(80, 90, inclusive='neither'), 'decil'] = '2'
result.loc[result['porc'].between(90, 101, inclusive='both'), 'decil'] = '1'

print(result.decil.value_counts().sort_index())
print(result[result.TGT == 1].decil.value_counts().sort_index())

a = result.groupby('decil')['Prob1'].agg('min')
print(a.sort_index())

decil
1     1624
10    1691
2     1623
3     1623
4     1624
5     1623
6     1623
7     1624
8     1623
9     1555
Name: count, dtype: int64
decil
1     1354
10       5
2     1048
3      815
4      594
5      423
6      308
7      159
8       53
9       17
Name: count, dtype: int64
decil
1     0.688578
10    0.012251
2     0.539833
3     0.426468
4     0.332262
5     0.251477
6     0.164156
7     0.075242
8     0.031342
9     0.014199
Name: Prob1, dtype: float32

##############################################
# test 

a = pd.DataFrame(X_test[[target_column, 'client_id']], columns=['TGT', 'idx'])
a = a.reset_index()
b = pd.DataFrame(probabilities[:,1], columns=['Prob1'])

result = pd.concat([a, b], axis=1)

result['porc'] = result['Prob1'].rank(pct=True) * 100

result['decil'] = np.where(result.Prob1 >= 0.688578               , 1, 
                            np.where((result.Prob1 >=  0.539833            ) & (result.Prob1 <  0.688578       ), 2,
                            np.where((result.Prob1 >=  0.426468               ) & (result.Prob1 < 0.539833                   ) , 3,
                            np.where((result.Prob1 >=  0.332262                ) & (result.Prob1 < 0.426468                 ), 4,
                            np.where((result.Prob1 >=  0.251477                 ) & (result.Prob1 < 0.332262               ), 5,
                            np.where((result.Prob1 >=  0.164156                ) & (result.Prob1 < 0.251477              ), 6,
                            np.where((result.Prob1 >=  0.075242                 ) & (result.Prob1 < 0.164156               ) , 7,
                            np.where((result.Prob1 >=  0.031342               ) & (result.Prob1 < 0.075242              ), 8,
                            np.where((result.Prob1 >=  0.014199                 ) & (result.Prob1 < 0.031342                    ), 9,
                            10)))))))))
print("Total")
print(result.decil.value_counts().sort_index())
print("Buenos")
print(result[result.TGT == 1].decil.value_counts())

Total
decil
1     694
2     702
3     697
4     676
5     664
6     703
7     697
8     711
9     672
10    742
Name: count, dtype: int64
Buenos
decil
1     531
2     452
3     340
4     278
5     173
6     149
7      89
8      28
9       4
10      3
Name: count, dtype: int64

y_test = X_test['TGT']
y_pred = model.predict(X_test[numerical_cols])

from sklearn.metrics import confusion_matrix


cm = confusion_matrix(y_test, y_pred)

print(cm)

[[4374  537]
 [ 943 1104]]

import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay

disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=model.classes_)
disp.plot()
plt.show()

threshold = 0.45
y_pred = (model.predict_proba(X_test[numerical_cols])[:, 1] > threshold).astype('float')
cm = confusion_matrix(y_test, y_pred)
disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=model.classes_)
disp.plot()
plt.show()

threshold = 0.42
y_pred = (model.predict_proba(X_test[numerical_cols])[:, 1] > threshold).astype('float')
cm = confusion_matrix(y_test, y_pred)
disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=model.classes_)
disp.plot()
plt.show()

# ROC

from sklearn.metrics import roc_auc_score, roc_curve, accuracy_score
import matplotlib.pyplot as plt
import numpy as np

yPred = y_pred
yScore = result['Prob1']
yTest = result['TGT']
areaBajoCurvaRoc = roc_auc_score(yTest, yScore)
accuracy = accuracy_score(yTest, yPred)
fpr, tpr, _ = roc_curve(yTest, yScore)
plt.plot(fpr, tpr)
plt.plot([0,1])
plt.xlabel('FPR')
plt.ylabel('TPR')
plt.annotate('Area bajo la curva ROC : {}'.format(areaBajoCurvaRoc), (-0.02,0.99))
plt.annotate('Accuracy : {}'.format(accuracy), (-0.02,0.94))
plt.show()
display()

pVar = 'Prob1'
result['porc'] = result[pVar].rank(pct=True) * 100

len(probabilities[:,1])

result.loc[result['porc'].between(0, 10, inclusive='neither'), 'decil'] = '10'
result.loc[result['porc'].between(10, 20, inclusive='both'), 'decil'] = '9'
result.loc[result['porc'].between(20, 30, inclusive='neither'), 'decil'] = '8'
result.loc[result['porc'].between(30, 40, inclusive='both'), 'decil'] = '7'
result.loc[result['porc'].between(40, 50, inclusive='neither'), 'decil'] = '6'
result.loc[result['porc'].between(50, 60, inclusive='both'), 'decil'] = '5'
result.loc[result['porc'].between(60, 70, inclusive='neither'), 'decil'] = '4'
result.loc[result['porc'].between(70, 80, inclusive='both'), 'decil'] = '3'
result.loc[result['porc'].between(80, 90, inclusive='neither'), 'decil'] = '2'
result.loc[result['porc'].between(90, 101, inclusive='both'), 'decil'] = '1'

a = pd.DataFrame(result.decil.value_counts().reset_index())
a.columns = ['index','decil']

b = pd.DataFrame(result[result.TGT == 1].decil.value_counts().reset_index())

b.columns = ['index','decil']

b

c = a.merge(b, how='left', on='index')
c['TGT_%'] = (c['decil_y'] /c['decil_x'])*100
c

	client_id	cantidad_meses
0	5856970	9
1	5895899	9
2	4712252	9
3	7304330	9
4	6657428	9
...	...	...
26555	6623284	1
26556	4424661	1
26557	5643352	1
26558	6641590	1
26559	264018	1

	client_id
2	5928737
10	6018047
11	5359038
16	6890812
20	115383
...	...
238573	6570413
238574	6258895
238585	6397274
238586	6007291
238612	6412619

	CreditCard_Spending_1_Installment	CreditCard_Spending_CrossBoarder	CreditCard_Spending_Aut_Debits	CreditCard_Spending_1_Installment_sum	CreditCard_Spending_1_Installment_max	CreditCard_Spending_1_Installment_min	CreditCard_Spending_1_Installment_mean	CreditCard_Spending_1_Installment_nunique	CreditCard_Spending_1_Installment_count_nonzero	CreditCard_Spending_1_Installment_var	...	CreditCard_Spending_CrossBoarder_nunique	CreditCard_Spending_CrossBoarder_count_nonzero	CreditCard_Spending_CrossBoarder_var	CreditCard_Spending_Aut_Debits_sum	CreditCard_Spending_Aut_Debits_max	CreditCard_Spending_Aut_Debits_min	CreditCard_Spending_Aut_Debits_mean	CreditCard_Spending_Aut_Debits_nunique	CreditCard_Spending_Aut_Debits_count_nonzero	CreditCard_Spending_Aut_Debits_var
0	-0.404539	-0.174669	-0.473121	-0.482228	-0.595466	-0.206982	-0.482228	-0.857834	-0.857969	-0.369477	...	-0.325674	-0.308900	-0.248440	-0.483256	-0.509510	-0.427386	-0.483256	-0.717120	-0.909830	-0.184095
1	-0.121738	-0.174669	4.916441	1.349612	2.198174	-0.206982	1.349612	1.619630	1.156892	1.813086	...	-0.325674	-0.308900	-0.248440	5.305475	4.312784	6.166697	5.305475	-0.181371	1.155444	-0.131284
2	0.115814	1.574087	-0.344315	-0.178073	-0.230906	-0.206982	-0.178073	1.619630	1.156892	-0.333223	...	1.156702	0.664959	-0.023213	-0.366725	-0.394261	-0.427386	-0.366725	-0.181371	0.811232	-0.176727
3	-0.404539	-0.174669	-0.473121	-0.482228	-0.595466	-0.206982	-0.482228	-0.857834	-0.857969	-0.369477	...	-0.325674	-0.308900	-0.248440	-0.483256	-0.509510	-0.427386	-0.483256	-0.717120	-0.909830	-0.184095
4	-0.404539	-0.174669	-0.473121	-0.482228	-0.595466	-0.206982	-0.482228	-0.857834	-0.857969	-0.369477	...	-0.325674	-0.308900	-0.248440	-0.483256	-0.509510	-0.427386	-0.483256	-0.717120	-0.909830	-0.184095

	CreditCard_Spending_1_Installment	CreditCard_Spending_CrossBoarder	CreditCard_Spending_Aut_Debits	CreditCard_Spending_1_Installment_sum	CreditCard_Spending_1_Installment_max	CreditCard_Spending_1_Installment_min	CreditCard_Spending_1_Installment_mean	CreditCard_Spending_1_Installment_nunique	CreditCard_Spending_1_Installment_count_nonzero	CreditCard_Spending_1_Installment_var	...	CreditCard_Spending_CrossBoarder_nunique	CreditCard_Spending_CrossBoarder_count_nonzero	CreditCard_Spending_CrossBoarder_var	CreditCard_Spending_Aut_Debits_sum	CreditCard_Spending_Aut_Debits_max	CreditCard_Spending_Aut_Debits_min	CreditCard_Spending_Aut_Debits_mean	CreditCard_Spending_Aut_Debits_nunique	CreditCard_Spending_Aut_Debits_count_nonzero	CreditCard_Spending_Aut_Debits_var
count	2.319100e+04	2.319100e+04	2.319100e+04	2.319100e+04	2.319100e+04	2.319100e+04	2.319100e+04	2.319100e+04	2.319100e+04	2.319100e+04	...	2.319100e+04	2.319100e+04	2.319100e+04	2.319100e+04	2.319100e+04	2.319100e+04	2.319100e+04	2.319100e+04	2.319100e+04	2.319100e+04
mean	5.361777e-18	-5.943913e-17	5.469013e-17	5.208584e-17	2.083433e-17	2.451098e-17	4.166867e-17	-6.556688e-17	1.715769e-17	-1.945559e-17	...	-1.862835e-16	4.657087e-17	-6.403494e-17	4.274103e-17	1.363423e-17	-8.885231e-18	1.838324e-17	-1.286827e-17	-2.849402e-17	2.757485e-18
std	1.000022e+00	1.000022e+00	1.000022e+00	1.000022e+00	1.000022e+00	1.000022e+00	1.000022e+00	1.000022e+00	1.000022e+00	1.000022e+00	...	1.000022e+00	1.000022e+00	1.000022e+00	1.000022e+00	1.000022e+00	1.000022e+00	1.000022e+00	1.000022e+00	1.000022e+00	1.000022e+00
min	-5.970550e+00	-1.746695e-01	-4.555431e+00	-7.962067e+00	-4.495013e+00	-1.319936e+01	-7.962067e+00	-8.578344e-01	-8.579690e-01	-3.694769e-01	...	-3.256741e-01	-3.089002e-01	-2.484396e-01	-1.050400e+00	-5.095100e-01	-5.763493e+00	-1.050400e+00	-7.171202e-01	-9.098305e-01	-1.840948e-01
25%	-4.045387e-01	-1.746695e-01	-4.731206e-01	-4.822276e-01	-5.954658e-01	-2.069816e-01	-4.822276e-01	-8.578344e-01	-8.579690e-01	-3.694769e-01	...	-3.256741e-01	-3.089002e-01	-2.484396e-01	-4.832563e-01	-5.095100e-01	-4.273857e-01	-4.832563e-01	-7.171202e-01	-9.098305e-01	-1.840948e-01
50%	-4.045387e-01	-1.746695e-01	-4.731206e-01	-4.631958e-01	-5.520753e-01	-2.069816e-01	-4.631958e-01	-3.623415e-01	-4.549967e-01	-3.693950e-01	...	-3.256741e-01	-3.089002e-01	-2.484396e-01	-4.832563e-01	-5.095100e-01	-4.273857e-01	-4.832563e-01	-7.171202e-01	-9.098305e-01	-1.840948e-01
75%	-1.798650e-01	-1.746695e-01	-4.200653e-02	1.850608e-02	1.678342e-01	-2.069816e-01	1.850608e-02	1.124137e+00	1.156892e+00	-2.023504e-01	...	-3.256741e-01	-3.089002e-01	-2.484396e-01	-7.949366e-03	3.423440e-02	-8.840142e-02	-7.949366e-03	8.901281e-01	1.155444e+00	-1.696582e-01
max	5.161473e+00	7.306930e+00	4.916441e+00	6.997612e+00	3.304082e+00	1.278540e+01	6.997612e+00	1.619630e+00	1.559865e+00	1.394463e+01	...	7.086209e+00	5.534253e+00	7.171873e+00	5.367874e+00	4.312784e+00	6.617469e+00	5.367874e+00	1.961627e+00	1.155444e+00	2.346509e+01

	PC1	PC2	PC3	PC4
CreditCard_Spending_1_Installment	0.210000	0.000000	-0.300000	0.180000
CreditCard_Spending_CrossBoarder	0.140000	0.240000	0.130000	0.050000
CreditCard_Spending_Aut_Debits	0.240000	-0.250000	0.200000	0.110000
CreditCard_Spending_1_Installment_sum	0.250000	0.010000	-0.330000	0.180000
CreditCard_Spending_1_Installment_max	0.250000	0.020000	-0.310000	-0.000000
CreditCard_Spending_1_Installment_min	0.160000	-0.000000	-0.260000	0.380000
CreditCard_Spending_1_Installment_mean	0.250000	0.010000	-0.330000	0.180000
CreditCard_Spending_1_Installment_nunique	0.220000	-0.030000	-0.190000	-0.470000
CreditCard_Spending_1_Installment_count_nonzero	0.230000	-0.040000	-0.190000	-0.430000
CreditCard_Spending_1_Installment_var	0.180000	0.040000	-0.230000	-0.010000
CreditCard_Spending_CrossBoarder_sum	0.190000	0.330000	0.180000	0.040000
CreditCard_Spending_CrossBoarder_max	0.200000	0.310000	0.160000	0.020000
CreditCard_Spending_CrossBoarder_min	0.060000	0.130000	0.090000	0.020000
CreditCard_Spending_CrossBoarder_mean	0.190000	0.330000	0.180000	0.040000
CreditCard_Spending_CrossBoarder_nunique	0.190000	0.250000	0.110000	-0.090000
CreditCard_Spending_CrossBoarder_count_nonzero	0.180000	0.240000	0.120000	-0.080000
CreditCard_Spending_CrossBoarder_var	0.180000	0.300000	0.150000	0.050000
CreditCard_Spending_Aut_Debits_sum	0.240000	-0.260000	0.210000	0.120000
CreditCard_Spending_Aut_Debits_max	0.240000	-0.260000	0.210000	0.100000
CreditCard_Spending_Aut_Debits_min	0.220000	-0.240000	0.200000	0.130000
CreditCard_Spending_Aut_Debits_mean	0.240000	-0.260000	0.210000	0.120000
CreditCard_Spending_Aut_Debits_nunique	0.210000	-0.180000	0.090000	-0.300000
CreditCard_Spending_Aut_Debits_count_nonzero	0.200000	-0.150000	0.050000	-0.400000
CreditCard_Spending_Aut_Debits_var	0.090000	-0.120000	0.100000	0.130000

	client_id	Target	Month	First_product_dt	Last_product_dt	CreditCard_Premium	CreditCard_Active	CreditCard_CoBranding	Loan_Active	Mortgage_Active	...	CreditCard_Payment_External	CreditCard_Payment_Cash	CreditCard_Payment_Web	CreditCard_Payment_ATM	CreditCard_Payment_TAS	Investment_Numbers	Mobile	Email	Region	CreditCard_Product
0	5856970	1.0	2018-10-01	2013-10-23	2019-01-10	No	Yes	No	No	No	...	0.0	0.0	0.0	0.0	0.0	1.0	Yes	Yes	NaN	NaN
1	6371753	0.0	2018-09-01	2015-07-29	2018-06-02	No	No	No	No	No	...	0.0	0.0	0.0	0.0	0.0	0.0	Yes	No	NaN	NaN
2	5928737	0.0	2019-01-01	2016-08-31	2018-12-27	No	No	No	No	No	...	0.0	0.0	0.0	0.0	0.0	0.0	Yes	Yes	NaN	NaN
3	475064	0.0	2018-12-01	2014-07-13	2017-11-30	No	Yes	No	No	No	...	0.0	0.0	0.0	0.0	0.0	0.0	Yes	Yes	NaN	NaN
4	3615172	0.0	2018-09-01	2017-12-27	2017-12-28	No	No	No	No	No	...	0.0	0.0	0.0	0.0	0.0	0.0	Yes	No	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
238597	1673642	0.0	2018-11-01	2017-08-18	2017-09-26	No	Yes	No	No	No	...	0.0	0.0	1.0	0.0	0.0	0.0	No	Yes	NaN	NaN
238603	6145735	1.0	2018-11-01	2014-10-26	2014-10-26	No	Yes	No	No	No	...	0.0	0.0	1.0	0.0	0.0	0.0	Yes	Yes	NaN	NaN
238604	5638786	1.0	2018-11-01	2012-12-26	2017-03-08	No	Yes	No	No	No	...	0.0	0.0	0.0	0.0	1.0	0.0	Yes	No	NaN	NaN
238608	3824781	0.0	2018-08-01	2014-11-27	2019-01-04	No	No	No	No	No	...	0.0	0.0	0.0	0.0	0.0	0.0	Yes	Yes	NaN	NaN
238612	6412619	0.0	2019-01-01	2015-07-08	2018-06-02	No	No	No	Yes	No	...	0.0	0.0	0.0	0.0	0.0	0.0	Yes	No	NaN	NaN

	client_id	Target	Investment_Numbers	CreditCard_Total_Limit
2	5928737	0.0	0.0	0.0
8	6018047	1.0	0.0	80000.0
9	5359038	1.0	0.0	64000.0
11	6890812	0.0	0.0	0.0
13	115383	0.0	0.0	0.0
...	...	...	...	...
139131	6570413	0.0	0.0	28000.0
139132	6258895	0.0	0.0	0.0
139138	6397274	0.0	0.0	40000.0
139139	6007291	0.0	0.0	96000.0
139145	6412619	0.0	0.0	0.0

	client_id	Target	Month	First_product_dt	Last_product_dt	CreditCard_Premium	CreditCard_Active	CreditCard_CoBranding	Loan_Active	Mortgage_Active	...	CreditCard_Payment_Web	Mobile	Email	Region	CreditCard_Product	cantidad_meses	TGT
50026	4473325	0.0	2018-12-01	2008-05-27	2017-05-18	Yes	Yes	No	No	No	...	0.0	Yes	Yes	NaN	NaN	9	0.0
50027	4837071	1.0	2018-12-01	2013-02-21	2018-09-04	No	No	No	No	No	...	0.0	Yes	Yes	NaN	NaN	9	1.0
50029	4525957	1.0	2019-01-01	2012-10-16	2016-08-31	Yes	Yes	No	No	No	...	1.0	Yes	Yes	NaN	NaN	9	1.0
139145	6412619	0.0	2019-01-01	2015-07-08	2018-06-02	No	No	No	Yes	No	...	0.0	Yes	No	NaN	NaN	9	0.0

	client_id	Target	Month	First_product_dt	Last_product_dt	CreditCard_Premium	CreditCard_Active	CreditCard_CoBranding	Loan_Active	Mortgage_Active	...	CreditCard_Payment_External	CreditCard_Payment_Cash	CreditCard_Payment_Web	CreditCard_Payment_ATM	CreditCard_Payment_TAS	Investment_Numbers	Mobile	Email	Region	CreditCard_Product
9	5775560	0.0	2019-04-01	2013-08-22	2014-08-01	No	Yes	No	No	No	...	0.0	0.0	0.0	0.0	0.0	0.0	Yes	Yes	REGION CENTRO	J55660104XX012
13	5800470	0.0	2019-04-01	2013-08-23	2018-03-26	No	Yes	Yes	No	No	...	0.0	0.0	0.0	0.0	0.0	0.0	Yes	Yes	REGION CENTRO	J55660123XX012
17	3540244	0.0	2019-04-01	2018-09-07	2018-09-07	No	No	No	No	No	...	0.0	0.0	0.0	0.0	0.0	0.0	No	No	REGION CENTRO	NaN
43	6912865	0.0	2019-04-01	2017-08-17	2019-01-04	No	No	No	No	No	...	0.0	0.0	0.0	0.0	1.0	0.0	Yes	Yes	BUENOS AIRES	NaN
47	6595044	0.0	2019-04-01	2016-01-14	2017-09-26	No	Yes	No	No	No	...	0.0	0.0	0.0	0.0	0.0	0.0	Yes	No	REGION CENTRO	J55660104XX012
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
238564	5678167	1.0	2019-04-01	2013-02-26	2018-11-28	No	Yes	No	No	No	...	0.0	0.0	0.0	0.0	1.0	0.0	No	No	BUENOS AIRES	J55660104XX012
238589	6948039	1.0	2019-04-01	2017-08-14	2019-01-10	No	Yes	No	No	No	...	0.0	0.0	0.0	0.0	0.0	0.0	Yes	Yes	REGION NORTE GRANDE ARGENTINO	J55660104XX012
238590	1818546	1.0	2019-04-01	2013-12-10	2018-02-07	No	Yes	No	No	No	...	0.0	1.0	0.0	0.0	0.0	0.0	Yes	Yes	REGION CUYO	J55660104XX012
238611	6377583	0.0	2019-04-01	2015-06-03	2019-01-04	No	Yes	No	Yes	No	...	0.0	0.0	0.0	0.0	0.0	0.0	Yes	Yes	BUENOS AIRES	J55660104XX012
238613	5542402	0.0	2019-04-01	2012-09-13	2012-09-13	No	Yes	No	No	No	...	0.0	0.0	0.0	1.0	0.0	0.0	No	Yes	REGION NORTE GRANDE ARGENTINO	J55660104XX012

	client_id	Month	First_product_dt	Last_product_dt	CreditCard_Premium	CreditCard_Active	CreditCard_CoBranding	Loan_Active	Mortgage_Active	...	Mobile	Email	Region	CreditCard_Product	cantidad_meses
5618	2181839	2018-08-01	2006-10-05	2006-10-05	No	No	No	No	No	...	No	No	NaN	NaN	9
12257	921872	2018-09-01	2005-11-24	2005-11-24	No	No	No	No	No	...	No	No	NaN	NaN	9
18700	2181839	2019-01-01	2006-10-05	2006-10-05	No	No	No	No	No	...	No	No	NaN	NaN	9
20460	436555	2018-11-01	2005-02-09	2005-02-09	No	No	No	No	No	...	No	No	NaN	NaN	9
25548	727081	2018-08-01	2005-07-18	2005-07-18	No	No	No	No	No	...	No	No	NaN	NaN	9
37210	535416	2018-09-01	2005-04-13	2005-04-13	No	No	No	No	No	...	No	No	NaN	NaN	9
37430	833913	2018-08-01	2005-08-31	2014-01-24	No	No	No	No	No	...	No	No	NaN	NaN	9
44841	436555	2018-08-01	2005-02-09	2005-02-09	No	No	No	No	No	...	No	No	NaN	NaN	9
53787	2181839	2018-09-01	2006-10-05	2006-10-05	No	No	No	No	No	...	No	No	NaN	NaN	9
55285	921872	2018-11-01	2005-11-24	2005-11-24	No	No	No	No	No	...	No	No	NaN	NaN	9
57045	436555	2018-10-01	2005-02-09	2005-02-09	No	No	No	No	No	...	No	No	NaN	NaN	9
62988	921872	2018-12-01	2005-11-24	2005-11-24	No	No	No	No	No	...	No	No	NaN	NaN	9
67184	833913	2019-01-01	2005-08-31	2014-01-24	No	No	No	No	No	...	No	No	NaN	NaN	9
77610	727081	2018-09-01	2005-07-18	2005-07-18	No	No	No	No	No	...	No	No	NaN	NaN	9
82529	2181839	2018-10-01	2006-10-05	2006-10-05	No	No	No	No	No	...	No	No	NaN	NaN	9
85105	436555	2018-09-01	2005-02-09	2005-02-09	No	No	No	No	No	...	No	No	NaN	NaN	9
87881	535416	2018-10-01	2005-04-13	2005-04-13	No	No	No	No	No	...	No	No	NaN	NaN	9
89371	2181839	2018-12-01	2006-10-05	2006-10-05	No	No	No	No	No	...	No	No	NaN	NaN	9
93734	833913	2018-11-01	2005-08-31	2014-01-24	No	No	No	No	No	...	No	No	NaN	NaN	9
93761	833913	2018-10-01	2005-08-31	2014-01-24	No	No	No	No	No	...	No	No	NaN	NaN	9
94231	2181839	2018-11-01	2006-10-05	2006-10-05	No	No	No	No	No	...	No	No	NaN	NaN	9
97122	727081	2018-11-01	2005-07-18	2005-07-18	No	No	No	No	No	...	No	No	NaN	NaN	9
99710	535416	2018-12-01	2005-04-13	2005-04-13	No	No	No	No	No	...	No	No	NaN	NaN	9
101314	535416	2019-01-01	2005-04-13	2005-04-13	No	No	No	No	No	...	No	No	NaN	NaN	9
101414	727081	2019-01-01	2005-07-18	2005-07-18	No	No	No	No	No	...	No	No	NaN	NaN	9
103617	727081	2018-10-01	2005-07-18	2005-07-18	No	No	No	No	No	...	No	No	NaN	NaN	9
105080	535416	2018-11-01	2005-04-13	2005-04-13	No	No	No	No	No	...	No	No	NaN	NaN	9
108222	833913	2018-09-01	2005-08-31	2014-01-24	No	No	No	No	No	...	No	No	NaN	NaN	9
109178	833913	2018-12-01	2005-08-31	2014-01-24	No	No	No	No	No	...	No	No	NaN	NaN	9
118693	921872	2018-10-01	2005-11-24	2005-11-24	No	No	No	No	No	...	No	No	NaN	NaN	9
120440	727081	2018-12-01	2005-07-18	2005-07-18	No	No	No	No	No	...	No	No	NaN	NaN	9
122807	436555	2018-12-01	2005-02-09	2005-02-09	No	No	No	No	No	...	No	No	NaN	NaN	9
130135	921872	2018-08-01	2005-11-24	2005-11-24	No	No	No	No	No	...	No	No	NaN	NaN	9
132195	921872	2019-01-01	2005-11-24	2005-11-24	No	No	No	No	No	...	No	No	NaN	NaN	9
135035	436555	2019-01-01	2005-02-09	2005-02-09	No	No	No	No	No	...	No	No	NaN	NaN	9
139104	535416	2018-08-01	2005-04-13	2005-04-13	No	No	No	No	No	...	No	No	NaN	NaN	9

	client_id	Target	CreditCard_Premium	CreditCard_Active	Loan_Active	Mortgage_Active	DebitCard_Active	Investment_Active	Sex	Client_Age_grp	...	Operations_Mobile	CreditCard_Balance_ARG	CreditCard_Balance_DOLLAR	CreditCard_Total_Limit	CreditCard_Total_Spending	CreditCard_Spending_1_Installment	CreditCard_Spending_CrossBoarder	CreditCard_Spending_Aut_Debits	CreditCard_Revolving	TGT
2	5928737	0.0	0	0	0	0	1	0	1	30	...	0.0	0.00	0.00	0.0	0.00	0.00	0.00	0.0000	0.00	0.0
8	6018047	1.0	1	1	0	0	1	0	1	60	...	0.0	16397.20	0.00	80000.0	16068.08	1000.00	0.00	12510.9125	-14.31	1.0
9	5359038	1.0	1	1	1	0	1	0	1	40	...	0.0	6906.73	33.67	64000.0	6919.64	1840.00	33.57	299.0000	0.00	1.0
11	6890812	0.0	0	0	1	0	1	0	1	40	...	0.0	0.00	0.00	0.0	0.00	0.00	0.00	0.0000	0.00	0.0
13	115383	0.0	0	0	0	0	0	0	1	70	...	0.0	0.00	0.00	0.0	0.00	0.00	0.00	0.0000	0.00	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
139131	6570413	0.0	0	1	0	0	1	0	0	30	...	1.0	537.43	0.00	28000.0	0.00	0.00	0.00	0.0000	0.00	0.0
139132	6258895	0.0	0	0	0	0	1	0	0	30	...	0.0	0.00	0.00	0.0	0.00	0.00	0.00	0.0000	0.00	0.0
139138	6397274	0.0	0	1	0	0	0	0	1	40	...	0.0	1475.18	0.00	40000.0	1384.27	0.00	0.00	459.0000	-77.32	0.0
139139	6007291	0.0	1	1	0	0	0	0	0	40	...	0.0	33148.43	0.00	96000.0	32977.86	11385.99	0.00	12510.9125	0.00	0.0
139145	6412619	0.0	0	0	1	0	1	0	0	60	...	0.0	0.00	0.00	0.0	0.00	0.00	0.00	0.0000	0.00	0.0

	client_id_sum	client_id_count_nonzero	TGT_sum	TGT_count_nonzero	Porcent_TGT
CreditCard_Product
J55660102XX012	1062656436	402	138.0	138	0.343284
J55660104XX012	34506107357	8262	3880.0	3880	0.469620
J55660123XX012	994616	1	0.0	0	0.000000
J55660124XX012	107695565	30	11.0	11	0.366667
J55660202XX012	23762080087	5759	2128.0	2128	0.369509
J55660702XX012	1060543322	249	108.0	108	0.433735
J55661002XX012	204312610	62	40.0	40	0.645161
No	40418495289	8426	518.0	518	0.061476

	client_id	SavingAccount_Days_with_Credits_sum	SavingAccount_Days_with_Credits_max	SavingAccount_Days_with_Credits_min	SavingAccount_Days_with_Credits_mean	SavingAccount_Days_with_Credits_nunique	SavingAccount_Days_with_Credits_count_nonzero	SavingAccount_Days_with_Credits_var	SavingAccount_Days_with_Debits_sum	SavingAccount_Days_with_Debits_max	...	CreditCard_Spending_Aut_Debits_nunique	CreditCard_Spending_Aut_Debits_count_nonzero	CreditCard_Spending_Aut_Debits_var	CreditCard_Revolving_sum	CreditCard_Revolving_max	CreditCard_Revolving_min	CreditCard_Revolving_mean	CreditCard_Revolving_nunique	CreditCard_Revolving_count_nonzero	CreditCard_Revolving_var
0	1030	13.0	3.0	1.0	2.166667	3	6	0.566667	17.000000	4.000000	...	2	6	337.500000	0.00	0.00	0.00	0.000000	1	0	0.000000e+00
1	1094	0.0	0.0	0.0	0.000000	1	0	0.000000	0.000000	0.000000	...	1	0	0.000000	7824.34	4121.95	-86.97	1304.056667	6	6	2.834001e+06
2	1553	0.0	0.0	0.0	0.000000	1	0	0.000000	0.000000	0.000000	...	1	0	0.000000	0.00	0.00	0.00	0.000000	1	0	0.000000e+00
3	1590	0.0	0.0	0.0	0.000000	1	0	0.000000	0.000000	0.000000	...	3	6	1414.566667	2874.30	2486.88	-45.10	479.050000	5	4	9.991904e+05
4	1948	8.0	2.0	1.0	1.333333	2	6	0.266667	11.000000	2.000000	...	6	6	187224.700000	20378.64	9969.35	-0.66	3396.440000	5	4	1.874074e+07
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
23186	7344944	7.0	5.0	0.0	1.166667	3	2	4.166667	8.000000	6.000000	...	1	0	0.000000	0.00	0.00	0.00	0.000000	1	0	0.000000e+00
23187	7345011	8.0	5.0	0.0	1.333333	3	2	4.666667	9.000000	5.000000	...	1	0	0.000000	0.00	0.00	0.00	0.000000	1	0	0.000000e+00
23188	7345026	15.0	5.0	0.0	2.500000	2	3	7.500000	12.000000	7.000000	...	1	0	0.000000	0.00	0.00	0.00	0.000000	1	0	0.000000e+00
23189	7345029	6.0	3.0	0.0	1.000000	2	2	2.400000	10.000000	6.000000	...	1	0	0.000000	0.00	0.00	0.00	0.000000	1	0	0.000000e+00
23190	7345492	10.0	5.0	0.0	1.666667	4	3	4.266667	18.723007	9.723007	...	1	0	0.000000	0.00	0.00	0.00	0.000000	1	0	0.000000e+00

	client_id	Operations_Bank	Operations_Terminal	Operations_HomeBanking	Operations_Mobile	Operations_Ivr	Operations_Telemarketer	Operations_ATM	TotalOperations	Porcent_Operations_HomeBanking	Porcent_Operations_Mobile
0	1030	0.0	0.0	6.0	1.0	0.0	1.0	0.0	8.0	0.750000	0.125000
1	1094	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.000000	0.000000
2	1553	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	1.000000	0.000000
3	1590	1.0	0.0	0.0	0.0	1.0	0.0	0.0	2.0	0.000000	0.000000
4	1948	0.0	0.0	8.0	1.0	0.0	0.0	0.0	9.0	0.888889	0.111111
...	...	...	...	...	...	...	...	...	...	...	...
23186	7344944	0.0	1.0	3.0	30.0	0.0	0.0	8.0	42.0	0.071429	0.714286
23187	7345011	2.0	1.0	1.0	3.0	0.0	0.0	7.0	14.0	0.071429	0.214286
23188	7345026	2.0	1.0	0.0	0.0	0.0	0.0	2.0	5.0	0.000000	0.000000
23189	7345029	2.0	1.0	0.0	0.0	0.0	0.0	6.0	9.0	0.000000	0.000000
23190	7345492	2.0	0.0	11.0	22.0	0.0	0.0	0.0	35.0	0.314286	0.628571

	client_id	CreditCard_Premium	CreditCard_Active	Loan_Active	Mortgage_Active	DebitCard_Active	Investment_Active	Sex	Client_Age_grp	Mobile	...	Porcent_Operations_HomeBanking	Porcent_Operations_Mobile	Insurance_Life	Insurance_Home	Insurance_Accidents	Insurance_Mobile	Insurance_ATM	Insurance_Unemployment	TotalInsurances	Porcent_Total_Insurances
count	2.319100e+04	23191.000000	23191.000000	23191.000000	23191.000000	23191.000000	23191.000000	23191.000000	23191.000000	23191.000000	...	23191.000000	23191.000000	23191.000000	23191.000000	23191.000000	23191.000000	23191.000000	23191.000000	23191.000000	23191.000000
mean	4.360437e+06	0.115346	0.647794	0.127981	0.022379	0.669527	0.054763	0.573541	44.405459	0.881075	...	0.283736	0.078601	0.193437	0.145272	0.156268	0.076409	0.143978	0.077918	0.793282	0.158656
std	2.144133e+06	0.319446	0.477668	0.334075	0.147917	0.470394	0.227521	0.494573	13.453333	0.323708	...	0.380932	0.213620	0.395001	0.352382	0.363117	0.265657	0.351075	0.268049	1.174822	0.234964
min	1.030000e+03	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	18.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	2.525100e+06	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	30.000000	1.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	4.991596e+06	0.000000	1.000000	0.000000	0.000000	1.000000	0.000000	1.000000	40.000000	1.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
75%	6.154208e+06	0.000000	1.000000	0.000000	0.000000	1.000000	0.000000	1.000000	50.000000	1.000000	...	0.619048	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	0.200000
max	7.345492e+06	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	70.000000	1.000000	...	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	6.000000	1.200000

	client_id	CreditCard_Premium	CreditCard_Active	Loan_Active	Mortgage_Active	DebitCard_Active	Investment_Active	Sex	Client_Age_grp	Mobile	...	Porcent_Operations_HomeBanking	Porcent_Operations_Mobile	Insurance_Life	Insurance_Home	Insurance_Accidents	Insurance_Mobile	Insurance_ATM	Insurance_Unemployment	TotalInsurances	Porcent_Total_Insurances
client_id	1.000000	0.044398	-0.148117	0.008420	-0.125800	0.149141	-0.035678	-0.058477	-0.302570	0.038438	...	0.003432	0.086509	-0.006130	-0.075573	-0.030472	-0.036124	0.013505	0.012061	-0.035528	-0.035528
CreditCard_Premium	0.044398	1.000000	0.254667	-0.007414	-0.015391	-0.099862	-0.035889	-0.026809	0.020644	0.003805	...	0.033540	0.028793	0.029580	0.021988	0.042374	0.079069	-0.047733	-0.007771	0.031480	0.031480
CreditCard_Active	-0.148117	0.254667	1.000000	0.038196	-0.052002	-0.285630	-0.038765	-0.017215	0.172011	0.012443	...	0.111196	0.078426	0.155410	0.158473	0.174625	0.191357	-0.019796	0.013618	0.194221	0.194221
Loan_Active	0.008420	-0.007414	0.038196	1.000000	-0.037892	0.223872	-0.040017	-0.011554	0.063915	0.065383	...	0.005896	0.058987	0.270208	0.158914	0.273786	0.067158	0.217906	0.708234	0.465033	0.465033
Mortgage_Active	-0.125800	-0.015391	-0.052002	-0.037892	1.000000	0.002179	0.019961	0.034384	0.002331	-0.029069	...	0.056854	0.005701	0.009304	0.045174	-0.003294	-0.021570	0.045069	-0.035281	0.016201	0.016201
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
Insurance_Mobile	-0.036124	0.079069	0.191357	0.067158	-0.021570	-0.058112	-0.019291	0.011712	0.132112	0.040485	...	-0.011414	0.007520	0.175155	0.250395	0.230260	1.000000	0.021671	0.060514	0.451573	0.451573
Insurance_ATM	0.013505	-0.047733	-0.019796	0.217906	0.045069	0.243219	0.037870	0.012652	-0.054322	0.058469	...	0.128352	0.097599	0.233253	0.149513	0.146543	0.021671	1.000000	0.176342	0.512532	0.512532
Insurance_Unemployment	0.012061	-0.007771	0.013618	0.708234	-0.035281	0.168320	-0.038151	-0.000777	-0.023123	0.051635	...	0.007100	0.051294	0.271025	0.107511	0.298883	0.060514	0.176342	1.000000	0.510293	0.510293
TotalInsurances	-0.035528	0.031480	0.194221	0.465033	0.016201	0.124437	-0.032341	0.032548	0.020222	0.085595	...	0.088078	0.091103	0.710251	0.589711	0.687889	0.451573	0.512532	0.510293	1.000000	1.000000
Porcent_Total_Insurances	-0.035528	0.031480	0.194221	0.465033	0.016201	0.124437	-0.032341	0.032548	0.020222	0.085595	...	0.088078	0.091103	0.710251	0.589711	0.687889	0.451573	0.512532	0.510293	1.000000	1.000000

	CreditCard_Premium	CreditCard_Active	Loan_Active	Mortgage_Active	DebitCard_Active	Investment_Active	Sex	Client_Age_grp	Mobile	Email	...	Porcent_Operations_HomeBanking	Porcent_Operations_Mobile	Insurance_Life	Insurance_Home	Insurance_Accidents	Insurance_Mobile	Insurance_ATM	Insurance_Unemployment	TotalInsurances	Porcent_Total_Insurances
CreditCard_Premium	1.000000	0.254667	0.007414	0.015391	0.099862	0.035889	0.026809	0.020644	0.003805	0.088081	...	0.033540	0.028793	0.029580	0.021988	0.042374	0.079069	0.047733	0.007771	0.031480	0.031480
CreditCard_Active	0.254667	1.000000	0.038196	0.052002	0.285630	0.038765	0.017215	0.172011	0.012443	0.089459	...	0.111196	0.078426	0.155410	0.158473	0.174625	0.191357	0.019796	0.013618	0.194221	0.194221
Loan_Active	0.007414	0.038196	1.000000	0.037892	0.223872	0.040017	0.011554	0.063915	0.065383	0.012032	...	0.005896	0.058987	0.270208	0.158914	0.273786	0.067158	0.217906	0.708234	0.465033	0.465033
Mortgage_Active	0.015391	0.052002	0.037892	1.000000	0.002179	0.019961	0.034384	0.002331	0.029069	0.007549	...	0.056854	0.005701	0.009304	0.045174	0.003294	0.021570	0.045069	0.035281	0.016201	0.016201
DebitCard_Active	0.099862	0.285630	0.223872	0.002179	1.000000	0.148153	0.022313	0.141464	0.118533	0.089266	...	0.178398	0.164068	0.051871	0.011021	0.018590	0.058112	0.243219	0.168320	0.124437	0.124437
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
Insurance_Mobile	0.079069	0.191357	0.067158	0.021570	0.058112	0.019291	0.011712	0.132112	0.040485	0.019767	...	0.011414	0.007520	0.175155	0.250395	0.230260	1.000000	0.021671	0.060514	0.451573	0.451573
Insurance_ATM	0.047733	0.019796	0.217906	0.045069	0.243219	0.037870	0.012652	0.054322	0.058469	0.060119	...	0.128352	0.097599	0.233253	0.149513	0.146543	0.021671	1.000000	0.176342	0.512532	0.512532
Insurance_Unemployment	0.007771	0.013618	0.708234	0.035281	0.168320	0.038151	0.000777	0.023123	0.051635	0.012599	...	0.007100	0.051294	0.271025	0.107511	0.298883	0.060514	0.176342	1.000000	0.510293	0.510293
TotalInsurances	0.031480	0.194221	0.465033	0.016201	0.124437	0.032341	0.032548	0.020222	0.085595	0.060886	...	0.088078	0.091103	0.710251	0.589711	0.687889	0.451573	0.512532	0.510293	1.000000	1.000000
Porcent_Total_Insurances	0.031480	0.194221	0.465033	0.016201	0.124437	0.032341	0.032548	0.020222	0.085595	0.060886	...	0.088078	0.091103	0.710251	0.589711	0.687889	0.451573	0.512532	0.510293	1.000000	1.000000

	client_id	TGT	CreditCard_Premium	CreditCard_Active	Loan_Active	Mortgage_Active	DebitCard_Active	Investment_Active	Sex	Client_Age_grp	...	Operations_Telemarketer	Operations_ATM	TotalOperations	Insurance_Life	Insurance_Home	Insurance_Accidents	Insurance_Mobile	Insurance_ATM	Insurance_Unemployment	TotalInsurances
0	5928737	0.0	-0.361090	-1.35619	-0.383097	-0.1513	0.702561	-0.240698	0.862295	-1.070796	...	-0.431961	-0.251284	-0.639048	-0.489723	-0.412265	-0.43036	-0.287629	-0.410115	-0.290693	-0.675250
1	6018047	1.0	2.769392	0.73736	-0.383097	-0.1513	0.702561	-0.240698	0.862295	1.159183	...	-0.431961	-0.251284	-0.196695	-0.489723	-0.412265	-0.43036	-0.287629	-0.410115	-0.290693	-0.675250
2	5359038	1.0	2.769392	0.73736	2.610303	-0.1513	0.702561	-0.240698	0.862295	-0.327469	...	-0.431961	1.489019	2.698708	-0.489723	-0.412265	-0.43036	-0.287629	-0.410115	-0.290693	-0.675250
3	6890812	0.0	-0.361090	-1.35619	2.610303	-0.1513	0.702561	-0.240698	0.862295	-0.327469	...	0.312898	-0.251284	2.296569	2.041969	-0.412265	-0.43036	-0.287629	-0.410115	3.440055	1.027172
4	115383	0.0	-0.361090	-1.35619	-0.383097	-0.1513	-1.423364	-0.240698	0.862295	1.902509	...	-0.431961	-0.251284	-0.639048	-0.489723	-0.412265	-0.43036	-0.287629	-0.410115	-0.290693	-0.675250

	rank	min	max	average	# of clients	# of target clients	target_p
0	1.0	-0.333423	-0.333423	-0.333423	7363	622	8.447644
1	3.0	-0.333423	-0.329075	-0.332007	1655	450	27.190332
2	4.0	-0.329074	-0.311315	-0.321289	2577	1069	41.482344
3	5.0	-0.311312	-0.272232	-0.294380	2577	1090	42.297245
4	6.0	-0.272174	-0.179269	-0.232096	2577	1074	41.676368
5	7.0	-0.179199	0.072656	-0.077905	2576	1085	42.119565
6	8.0	0.072684	1.204171	0.459300	2577	1049	40.706248
7	9.0	1.205259	24.458540	3.263167	1289	384	29.790535

	column	order
0	CreditCard_Balance_ARG_var	62.0
1	TotalOperations	61.0
2	CreditCard_Total_Limit	58.0
3	CreditCard_Total_Spending	50.0
4	SavingAccount_Total_Amount	48.0
5	Operations_Bank	44.0
6	Client_Age_grp	44.0
7	Operations_Terminal	42.0
8	CreditCard_Total_Spending_var	40.0
9	Operations_HomeBanking_var	40.0
10	CreditCard_Revolving_min	38.0
11	CreditCard_Total_Limit_var	38.0
12	CreditCard_Revolving	37.0
13	CreditCard_Spending_Aut_Debits_var	36.0
14	SavingAccount_Transactions_Transactions_var	35.0
15	SavingAccount_Total_Amount_var	34.0
16	CreditCard_Balance_ARG	33.0
17	CreditCard_Revolving_var	31.0
18	CreditCard_Spending_Aut_Debits	30.0
19	SavingAccount_Days_with_Credits_var	29.0
20	SavingAccount_Credits_Transactions_var	27.0
21	CreditCard_Balance_DOLLAR_sum	24.0
22	TotalInsurances	23.0
23	Operations_Telemarketer	21.0
24	Operations_Mobile_var	21.0
25	CreditCard_Active	20.0
26	SavingAccount_Transfer_In_Amount_var	19.0
27	SavingAccount_DebitCard_Spend_Transactions	19.0
28	SavingAccount_DebitCard_Spend_Amount_var	17.0
29	SavingAccount_Salary_Payment_Amount_var	17.0
30	CreditCard_Spending_1_Installment_min	17.0
31	SavingAccount_Days_with_Debits_var	17.0
32	Operations_ATM	16.0
33	SavingAccount_Active_ARG_Salary	15.0
34	SavingAccount_Transfer_Out_Amount_sum	15.0
35	SavingAccount_Salary_Payment_Transactions	15.0
36	CreditCard_Spending_Aut_Debits_nunique	15.0
37	Operations_HomeBanking_x	15.0
38	DebitCard_Active	14.0
39	Loan_Active	14.0
40	SavingAccount_Days_with_Debits_nunique	13.0
41	SavingAccount_CreditCard_Payment_Amount	12.0
42	Operations_Ivr	11.0
43	CreditCard_Spending_1_Installment_nunique	10.0
44	Region_REGION CUYO	10.0
45	CreditCard_Spending_Aut_Debits_count_nonzero	10.0
46	Mobile	10.0
47	SavingAccount_Days_with_Credits_count_nonzero	10.0
48	Region_BUENOS AIRES	9.0
49	Region_REGION CENTRO	9.0

	rank	min	max	average	# of clients	# of target clients	target_p
0	1.0	-0.878796	-0.878796	-0.878796	7427	554	7.459270
1	3.0	-0.869794	-0.446705	-0.554437	1748	546	31.235698
2	4.0	-0.374690	-0.302674	-0.328121	1599	589	36.835522
3	5.0	-0.158644	-0.014614	-0.081112	3851	1528	39.678006
4	6.0	0.021394	0.201432	0.129444	1311	589	44.927536
5	7.0	0.273447	0.561508	0.402769	3442	1475	42.852992
6	8.0	0.849568	1.641735	1.109193	2373	990	41.719343
7	9.0	2.001811	4.162266	2.878371	1440	552	38.333333

	rank	min	max	average	# of clients	# of target clients	target_p
0	0.0	-2.069368	-0.650194	-0.842358	18	3	16.666667
1	2.0	-0.649929	-0.649929	-0.649929	9377	837	8.926096
2	4.0	-0.649781	-0.439504	-0.546846	2201	741	33.666515
3	5.0	-0.439478	-0.174241	-0.308974	2576	1067	41.420807
4	6.0	-0.174133	0.155760	-0.017973	2577	1156	44.858362
5	7.0	0.156280	0.680816	0.387786	2576	1197	46.467391
6	8.0	0.680919	2.010680	1.194128	2577	1189	46.138921
7	9.0	2.011431	4.315257	3.164614	1289	633	49.107836

	rank	min	max	average	# of clients	# of target clients	target_p
0	2.0	-0.535720	-0.535720	-0.535720	11770	2409	20.467290
1	5.0	-0.535719	-0.353948	-0.458817	2402	532	22.148210
2	6.0	-0.353872	-0.113505	-0.246878	2577	712	27.629026
3	7.0	-0.113304	0.487357	0.127233	2576	1060	41.149068
4	8.0	0.487399	3.069775	1.447382	2577	1385	53.744664
5	9.0	3.071361	3.092456	3.092357	1289	725	56.245151

	rank	min	max	average	# of clients	# of target clients	target_p
0	3.0	-0.339982	-0.339982	-0.339982	17488	4236	24.222324
1	7.0	0.105446	0.105446	0.105446	2475	1032	41.696970
2	8.0	0.550874	1.441731	0.841044	2014	904	44.885799
3	9.0	1.887159	42.421137	3.287287	1214	651	53.624382

	rank	min	max	average	# of clients	# of target clients	target_p
0	0.0	-1.962787	-1.962787	-1.962787	989	249	25.176946
1	1.0	-1.070796	-1.070796	-1.070796	5308	1214	22.871138
2	4.0	-0.327469	-0.327469	-0.327469	6563	1660	25.293311
3	6.0	0.415857	0.415857	0.415857	5149	1796	34.880559
4	7.0	1.159183	1.159183	1.159183	2122	776	36.569274
5	8.0	1.530846	1.530846	1.530846	1745	694	39.770774
6	9.0	1.902509	1.902509	1.902509	1315	434	33.003802

	rank	min	max	average	# of clients	# of target clients	target_p
0	3.0	-0.485396	-0.485396	-0.485396	14773	3178	21.512218
1	6.0	-0.245225	-0.245225	-0.245225	1741	605	34.750144
2	7.0	-0.005054	0.715460	0.344768	2992	1123	37.533422
3	8.0	0.955631	1.676145	1.169663	2490	1161	46.626506
4	9.0	1.916316	24.732580	3.057481	1195	756	63.263598

	rank	min	max	average	# of clients	# of target clients	target_p
0	2.0	-0.364309	-0.364309	-0.364309	8555	738	8.626534
1	3.0	-0.364309	-0.363583	-0.364076	463	150	32.397408
2	4.0	-0.363579	-0.340502	-0.354216	2577	992	38.494373
3	5.0	-0.340495	-0.289687	-0.318348	2577	1111	43.112146
4	6.0	-0.289648	-0.173521	-0.239913	2577	1133	43.965852
5	7.0	-0.173507	0.116739	-0.055659	2576	1082	42.003106
6	8.0	0.116846	1.379620	0.563281	2577	1096	42.530074
7	9.0	1.380478	16.479469	3.358022	1289	521	40.418929

	rank	min	max	average	# of clients	# of target clients	target_p
0	2.0	-0.379385	-0.379385	-0.379385	12506	2933	23.452743
1	5.0	-0.326504	-0.326504	-0.326504	1995	564	28.270677
2	6.0	-0.294776	-0.167862	-0.256302	2021	580	28.698664
3	7.0	-0.167862	0.181151	-0.046336	2766	968	34.996385
4	8.0	0.181151	1.524321	0.662021	2610	1173	44.942529
5	9.0	1.524321	16.806844	3.336611	1293	605	46.790410

	rank	min	max	average	# of clients	# of target clients	target_p
0	0.0	-6.168433	-0.712710	-2.280568	1288	571	44.332298
1	1.0	-0.712662	-0.086175	-0.278155	2577	1104	42.840512
2	2.0	-0.086037	-0.026004	-0.042562	2577	998	38.727202
3	3.0	-0.026000	-0.020023	-0.021423	2574	823	31.973582
4	4.0	-0.020021	-0.019922	-0.019969	571	143	25.043783
5	6.0	-0.019921	-0.019921	-0.019921	11110	2301	20.711071
6	8.0	-0.019919	1.108721	0.387373	1205	427	35.435685
7	9.0	1.112892	6.128592	2.781178	1289	456	35.376261

Trabajo Practico¶

Desarrollo de Modelos Analíticos (Machine Learning) K5051 -2024¶

Grupo 3¶

Choque Llanqui, Edson Gustavo¶

Nigliazzo, Matias Ezequiel¶

Sanchez, Tomas Agustin¶

Exploratory Data Analysis¶

Identity features del ultimo mes de training window¶

Transform Features del ultimo mes de training window¶

Missing values¶

Tratamiento missing: SavingAccount_Balance_Average¶

Tratamiento missing: Region¶

Missing values Region que no se pudieron completar¶

Tratamiento missing: CreditCard_Product¶

Missing values CreditCard_Product que no se pudieron completar¶

Outliers¶

Analisis de Outliers de variables 1 al 30¶

quitando outliers Variable: SavingAccount_Days_with_Credits¶

quitando outliers Variable: SavingAccount_Days_with_Debits¶

quitando outliers Variable: SavingAccount_Salary_Payment_Transactions¶

quitando outliers Variable: SavingAccount_Transfer_In_Transactions¶

quitando outliers Variable: SavingAccount_ATM_Extraction_Transactions¶

quitando outliers Variable: SavingAccount_CreditCard_Payment_Transactions¶

quitando outliers Variable: SavingAccount_Transfer_Out_Transactions¶

quitando outliers Variable: SavingAccount_DebitCard_Spend_Transactions¶

quitando outliers Variable: SavingAccount_Transactions_Transactions¶

quitando outliers Variable: SavingAccount_Credits_Transactions¶

quitando outliers Variable: SavingAccount_Debits_Transactions¶

quitando outliers Variable: SavingAccount_Salary_Payment_Amount¶

quitando outliers Variable: SavingAccount_Transfer_In_Amount¶

quitando outliers Variable: SavingAccount_ATM_Extraction_Amount¶

quitando outliers Variable: SavingAccount_CreditCard_Payment_Amount¶

quitando outliers Variable: SavingAccount_Transfer_Out_Amount¶

quitando outliers Variable: SavingAccount_DebitCard_Spend_Amount¶

quitando outliers Variable: SavingAccount_Total_Amount¶

quitando outliers Variable: SavingAccount_Credits_Amounts¶

quitando outliers Variable: Operations_HomeBanking¶

quitando outliers Variable: Operations_Mobile¶

quitando outliers Variable: CreditCard_Balance_ARG¶

quitando outliers Variable: CreditCard_Balance_DOLLAR¶

quitando outliers Variable: CreditCard_Total_Limit¶

quitando outliers Variable: CreditCard_Total_Spending¶

quitando outliers Variable: CreditCard_Spending_1_Installment¶

quitando outliers Variable: CreditCard_Spending_CrossBoarder¶

quitando outliers Variable: CreditCard_Spending_Aut_Debits¶

Transform Features¶

variables con valores (Yes/No), (M/F) y las edades¶

One Hot Encoding en variable Region¶

Identity Features to ABT¶

Tratamiento de variables con fechas¶

Transformar varible CreditCard_Product¶

Aggregate Features to ABT¶

Create ABT¶

Dimensionality Reduction¶

Minimum = Maximum¶

PCA¶

Correlated Features¶

Feature selection¶

searchCV¶

importance feature¶

Bivariate Analysis¶

Select the main features¶

Split in Train and Test¶

Modelling¶

Train¶

Test¶

Deciles¶

Performance¶

	rank	min	max	average	# of clients	# of target clients	target_p
0	3.0	-0.216976	-0.216976	-0.216976	16387	5216	31.830109
1	6.0	-0.216952	-0.195942	-0.206782	335	78	23.283582
2	7.0	-0.195438	-0.009670	-0.111716	2603	683	26.238955
3	8.0	-0.008997	0.644553	0.214651	2574	623	24.203574
4	9.0	0.644553	29.465396	2.603049	1292	223	17.260062

	rank	min	max	average	# of clients	# of target clients	target_p
0	0.0	-5.612467	-0.336828	-0.883084	1288	528	40.993789
1	1.0	-0.336769	-0.295570	-0.302413	2575	974	37.825243
2	2.0	-0.295569	-0.295435	-0.295494	900	302	33.555556
3	4.0	-0.295434	-0.295434	-0.295434	13356	3180	23.809524
4	7.0	-0.295432	-0.122488	-0.253519	1206	461	38.225539
5	8.0	-0.122321	2.042820	0.750355	2577	947	36.748157
6	9.0	2.043695	5.021600	3.491048	1289	431	33.436773

	rank	min	max	average	# of clients	# of target clients	target_p
0	2.0	-0.324941	-0.324941	-0.324941	10935	2277	20.823045
1	4.0	-0.319016	-0.319016	-0.319016	646	119	18.421053
2	5.0	-0.315461	-0.285836	-0.302442	2609	702	26.906861
3	6.0	-0.282281	-0.211182	-0.251483	2572	805	31.298600
4	7.0	-0.211182	0.025817	-0.118617	2564	999	38.962559
5	8.0	0.025817	1.344714	0.488846	2576	1274	49.456522
6	9.0	1.348269	18.682349	3.289422	1289	647	50.193949

	rank	min	max	average	# of clients	# of target clients	target_p
0	2.0	-0.335904	-0.335904	-0.335904	10684	2404	22.500936
1	4.0	-0.335904	-0.335896	-0.335903	911	91	9.989023
2	5.0	-0.335896	-0.327482	-0.333761	2577	578	22.429181
3	6.0	-0.327476	-0.274374	-0.306968	2577	868	33.682577
4	7.0	-0.274294	-0.001548	-0.171869	2576	1084	42.080745
5	8.0	-0.001374	1.575777	0.513347	2577	1308	50.756694
6	9.0	1.585892	7.529617	3.619710	1289	490	38.013964

	rank	min	max	average	# of clients	# of target clients	target_p
0	0.0	-5.548031	-0.653676	-0.741103	766	144	18.798956
1	2.0	-0.653675	-0.653675	-0.653675	7961	677	8.503957
2	3.0	-0.653674	-0.626485	-0.645485	291	63	21.649485
3	4.0	-0.626203	-0.418643	-0.519399	2577	955	37.058595
4	5.0	-0.418577	-0.188284	-0.307576	2577	1081	41.948002
5	6.0	-0.188089	0.130912	-0.044256	2577	1148	44.547924
6	7.0	0.130944	0.686263	0.380433	2576	1120	43.478261
7	8.0	0.686605	2.065600	1.231719	2577	1133	43.965852
8	9.0	2.066048	4.240681	3.142321	1289	502	38.944919

	rank	min	max	average	# of clients	# of target clients	target_p
0	2.0	-0.283414	-0.283414	-0.283414	9790	1830	18.692543
1	4.0	-0.283414	-0.283410	-0.283413	1805	558	30.914127
2	5.0	-0.283410	-0.281129	-0.282967	2577	1031	40.007761
3	6.0	-0.281127	-0.245192	-0.268607	2577	1028	39.891347
4	7.0	-0.245144	-0.059331	-0.177578	2576	995	38.625776
5	8.0	-0.059146	1.110684	0.320401	2577	997	38.688397
6	9.0	1.120624	16.779817	3.366449	1289	384	29.790535

	rank	min	max	average	# of clients	# of target clients	target_p
0	0.0	-4.555431	-0.508876	-0.892626	18	4	22.222222
1	3.0	-0.473121	-0.473121	-0.473121	13065	2183	16.708764
2	5.0	-0.473078	-0.370593	-0.410499	1090	466	42.752294
3	6.0	-0.370158	-0.146712	-0.283454	2576	1115	43.284161
4	7.0	-0.146501	0.457383	0.121738	2576	1188	46.118012
5	8.0	0.457943	1.952927	1.027224	2577	1210	46.953822
6	9.0	1.955667	4.916441	3.424559	1289	657	50.969744

	rank	min	max	average	# of clients	# of target clients	target_p
0	2.0	-0.514025	-0.514025	-0.514025	11877	2531	21.310095
1	5.0	-0.338605	-0.233353	-0.324791	2141	656	30.639888
2	6.0	-0.233353	0.082402	-0.115928	3101	1032	33.279587
3	7.0	0.082402	0.503409	0.260621	2331	850	36.465036
4	8.0	0.503409	1.766431	0.953721	2422	1120	46.242775
5	9.0	1.766431	13.554633	3.216469	1319	634	48.066717

	rank	min	max	average	# of clients	# of target clients	target_p
0	2.0	-0.466557	-0.466557	-0.466557	11555	2392	20.700995
1	4.0	-0.386273	-0.386273	-0.386273	3	0	0.000000
2	5.0	-0.386273	-0.322046	-0.359128	2663	760	28.539241
3	6.0	-0.322046	-0.129366	-0.176838	2530	826	32.648221
4	7.0	-0.129366	0.304165	0.102248	2573	976	37.932375
5	8.0	0.304165	1.733210	0.861197	2578	1224	47.478666
6	9.0	1.733210	14.177146	3.345793	1289	645	50.038790

	rank	min	max	average	# of clients	# of target clients	target_p
0	0.0	-13.787115	-0.220613	-1.725336	269	116	43.122677
1	4.0	-0.220613	-0.220613	-0.220613	19933	5492	27.552300
2	8.0	-0.220443	1.497041	0.372545	1700	700	41.176471
3	9.0	1.500672	13.345889	3.280270	1289	515	39.953452